phd quantitative analysis position

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Quantitative methods, doctor of philosophy (ph.d.), you are here, a doctoral program focused on measurement and evaluation that trains students to create new research methodologies and design empirical data analyses. .

The Quantitative Methods Ph.D. program is designed to prepare future professors at research universities and principal investigators at research and assessment organizations in education, psychology, and related human services fields.

What Sets Us Apart

About the program.

Rigorous coursework across the field of education will prepare students with the tools needed to conduct cutting-edge research and assessment.  

Fall: 4 courses; Spring: 4 courses

Research apprenticeship Yes

Culminating experience Dissertation

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities as well as important responsibilities at research and assessment organizations. Graduates will be prepared to design first-rate empirical research and data analyses and to contribute to the development of new research methodologies. Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for the  M.S.Ed. degree in Statistics, Measurement, Assessment, and Technology (SMART)  and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation. 

For information about courses and requirements, visit the  Quantitative Methods Ph.D. program in the University Catalog .

Our Faculty

Affiliated Faculty

Eric T. Bradlow K.P. Chao Professor, The Wharton School Ph.D., Harvard University

Timothy Victor   Adjunct Associate Professor, Penn GSE 

"Penn GSE’s Quantitative Methods Ph.D. program equipped me with the methodological skills to do impactful applied education research as soon as I graduated."

Anna Rhoad-Drogalis

Our graduates.

Graduates go on to careers as university professors, researchers and psyshometricians for government agencies, foundations, nonprofits organizations, and corporations. 

Alumni Careers

• Assistant Professor, Texas A&M University-Corpus Christi
• Associate Director, Bristol-Myers Squibb
• Lead Psychometrician, American Institute of Certified Public Accountants
• Research Analyst, Penn Child Research Center, University of Pennsylvania
• Senior Director, Educational Testing Service
• Senior Researcher, Mathematica

Admissions & Financial Aid

Please visit our Admissions and Financial Aid pages for specific information on the application requirements , as well as information on tuition, fees, financial aid, scholarships, and fellowships.

Contact us if you have any questions about the program.

Graduate School of Education University of Pennsylvania 3700 Walnut Street Philadelphia, PA 19104 (215) 898-6415 [email protected] [email protected]

Christine P. Lee Program Manager (215) 898-0505 [email protected]

Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.

All Ph.D. students are guaranteed a full scholarship for their first four years of study, as well as a stipend and student health insurance. Penn GSE is committed to making your graduate education affordable, and we offer generous scholarships, fellowships, and assistantships.

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You May Be Interested In

Related programs.

• Education Policy M.S.Ed. 
• Education, Culture, and Society Ph.D. 
• Higher Education Ph.D. 
• Quantitative Methods M.Phil.Ed.
• Statistics, Measurement, Assessment, and Research Technology M.S.Ed.

Related Topics

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• Graduate Catalog /
• Graduate School of Education /

Quantitative Methods, PhD

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities and important responsibilities at research and assessment organizations. Graduates will be prepared to design first rate empirical research and data analyses and to contribute to development of new research methodologies.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation.

Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for M.S.Ed. degree in SMART and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

For more information: http://www.gse.upenn.edu/qm/phd

View the University’s Academic Rules for PhD Programs .

The Ph.D. degree program in Quantitative Methods requires a minimum of 20 course units or relevant courses and advanced degree accomplishments. A maximum of eight (8) credits from other institutions may be taken into account in reducing this basic requirement where appropriate.

Required Milestones

Qualifications evaluation (also known as program candidacy).

A Qualifications Evaluation of each student is conducted after the completion of 6 but not more than 8 course units. The evaluation is designed by the specialization faculty and may be based on an examination or on a review of a student’s overall academic progress.

Preliminary Examination (Also known as Doctoral Candidacy)

A Candidacy Examination on the major subject area is required.  The candidacy examination is a test of knowledge in the student's area of specialization, requiring students to demonstrate knowledge and reasoning in the key content areas in their specialization as defined by their academic division. This examination is normally held after the candidate has completed all required courses.

Oral Proposal

All doctoral candidates must present their dissertation proposals orally and in person to the dissertation committee.

Final Defense of the Dissertation

The final dissertation defense is approximately two hours in length and is based upon the candidate’s dissertation. 

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

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PhD scholarship in “Quantitative Analysis of Impact Investing and Social Entrepreneurship Scaling”

We are looking for a highly motivated PhD candidate for a PhD scholarship in “Quantitative Analysis of Impact Investing and Social Entrepreneurship Scaling”. The scholarship is part of the seizmic Doctoral Network, based on a grant from the Marie Sk?odowska-Curie Actions (MSCA) Doctoral Network call 2023. Are you passionate about social entrepreneurship and impact investing? Do you have experience with quantitative research methods? Consider applying for this PhD position!

• Hi! Are you my new colleague? Hannie Lensink
• Hi! Are you my new colleague? Kirsten Stadermann
• Hi! Are you my new colleague? Tanya Bondarouk
• Hi! Are you my new colleague? Ipek Topan
• Hi! Are you my new colleague? Abhishta Abhishta
• Hi! Are you my new colleague? Henk Pol
• Hi! Are you my new colleague? Theodora Spirova

Key takeaways

• Hours 38 - 40 hr.
• Salary indication Salary gross/monthly based on full-time € 2,872 - € 3,670
• Deadline 1 Oct 2024

The PhD project is part of a network of 15 doctoral theses that all contribute to the overall goal of understanding social entrepreneurship (SE) scaling. This particular PhD thesis is part of the work package on “Impact Investing for Social Entrepreneurship Scaling”. The position is focused on quantitatively assessing the impact of hybrid financing strategies, Schumpeterian 'creative destruction' logics, and impact fidelity on the scaling of social enterprises. This research will involve a systematic literature review of empirical papers on SE scaling and impact investing, followed by developing measures to quantitatively test these factors through a cross-sectional survey of impact investors. More specifically, the PhD candidate will explore the connection between business model aspects and impact investment decisions by looking into investors’ perceived success factors for social entrepreneurship. You will use structural equation modeling (SEM) and other quantitative research methods to probe the connections between these factors, offering new insights into how they shape the trajectory of SE scaling. The research predominantly attempts to contribute to the Social and Sustainable Entrepreneurship literature.

As required by the MSCA doctoral network funding guidelines, the PhD candidate will have both an academic secondment at one of the seizmic doctoral network members and a non-academic secondment providing a blend of academic and practical perspectives.

The PhD candidate will be guided by a multidisciplinary supervisory team with rich experience in strategic and sustainable entrepreneurship and innovation management. Dr. Svenja Damberg will be the daily supervisor, while Dr. Michel Ehrenhard will fulfill the role of promotor.

The successful applicant will join the Entrepreneurship and Technology Management (ETM) section of the High-tech Business & Entrepreneurship (HBE) department at the Faculty of Behavioural, Management and Social Sciences.

Your profile

We are looking for a positive, highly dedicated researcher who is driven by curiosity and has/is:

• A Master's degree in Business Administration, Entrepreneurship, Innovation Management, Marketing, Industrial Engineering & Management, Sociology, or related fields;
• Close rapport with quantitative research and a proven set of skills;
• Highly motivated to conduct high-quality, ‘engaged’ and impact-driven academic research resulting in scientific publications, which is reflected in study results, personal skills, and experience;
• Able to carry out independent research and collaborate with a wide set of actors from academia and practice;
• Outstanding team spirit and seeks to work in an interdisciplinary team;
• Excellent oral and written language skills in English and preferably Dutch.

We encourage high responsibility and independence while collaborating with colleagues, researchers, other university staff, and partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a full-time 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:

• Gross monthly salary of € 2.872 in the first year, increasing each year up to € 3.670 in the fourth year;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• 29 holidays per year in case of full-time employment;
• A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• A green campus with free access to sports facilities and an international scientific community;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• Full status as an employee at the UT, including pension, health care benefits, and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.

Information and application

We are eager to hear from you and welcome your application! Please submit your application before 1 October 2024 and include:

• A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;
• A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references;
• If available, a PDF of the master thesis;
• An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants with a non-Dutch qualification and who have not had secondary and tertiary education in English can only be admitted with an IELTS-test showing a total band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).

For any inquiries about this position, you can contact dr. Svenja Damberg ([email protected]) or Michel Ehrenhard ([email protected]).

Additional MSCA Application Requirements

According to the mobility requirements of the MSCA guidelines candidates must not have resided or carried out their main activity (work, studies, etc.) in the country of the host organization (i.e., The Netherlands in this case) for more than 12 months in the 3 years immediately before the recruitment date.

We aim to treat your application as soon as possible and respond swiftly. The first round of interviews will be held in the end of October. Upon positive evaluation, you are invited for a second interview, which will take place in November. We aim for the applicant to start by February 2025.

More information about the network can be found on the website: www.seizmic.eu/msca.

Share this vacancy

About the department.

The HBE department is dedicated to encouraging a supportive and inclusive working culture. Our aim is that all job applicants are given equal opportunities. When we select candidates for employment, it will be on the basis of their competence and ability. To support workforce diversity, we are open to offering flexible working conditions on an individual basis to support work-life balance, which may include contract of employment, working hours and location, or childcare arrangements.

The High Business Entrepreneurship corporate video can be watched via this link .

About the organisation

The Faculty of Behavioral, Management and Social sciences (BMS) aims to play a key role in understanding, jointly developing and evaluating innovations in society. Technological developments are the engine of innovation. As a technical university that puts people first, we tailor them to human needs and behavior and use social engineering to integrate them into society. We also ensure adequate governance at public and private level, and robust, inclusive and fair organizational structures. We do this by developing, sharing and applying high-quality knowledge in Psychology, Business Administration, Public Administration, Communication Sciences, Philosophy, Educational Sciences and Health Sciences. Our research and education in these disciplines revolves around tackling and solving societal challenges. The research programs of BMS are closely linked to the research of the UT institutes Mesa+ Institute for Nanotechnology, TechMed Center and Digital Society Institute.

As an employer, the Faculty of BMS offers work that matters. We equip you to create new possibilities for yourself and for our society. With us, you will become part of a leading technical university with increasing, positive social impact. We offer an open, inclusive and entrepreneurial atmosphere, in which we encourage you to make healthy choices, for example through our flexible, adaptable benefits.

Want to know more?

Damberg, S. (Svenja) Assistant Professor

Do you have questions about this vacancy? Then you can contact  Svenja  for all substantive questions about this position and the application procedure. For general questions about working for the UT, please refer to the chatbot.

How to apply

Apply. When you see a vacancy that appeals to you, you can apply online. We ask you to upload a CV and motivation letter and/or list of publications. You will receive a confirmation of receipt by e-mail.

Selection. The selection committee will review your application and you will receive a response within 2 weeks after the vacancy has been closed.

1st interview. The 1st (online or in person) meeting serves as an introduction where we introduce ourselves to you and you to us. You may be asked to give a short presentation. This will be further explained in the invitation.

2nd interview. In the second interview, we will further discuss the job content, your skills and your talents.

The offer. If the conversations are positive, you will be made a suitable offer. If applicable, we will sign you up for screening.

Your Colleagues

Ehrenhard, M.L. (Michel)

About  the bms  faculty.

Curious about what The Faculty of Behavioral, Management and Social sciences (BMS) stands for? Check out the BMS website for more information.

A job that matters

Create new opportunities for yourself, your colleagues and our society. Scientist or teacher, administrator or facilitator, thinker or doer, energetic leader or silent support – your work at the University of Twente matters. And you too!

Our mission Human Touch

At the UT it’s all about people, in line with our university’s High Tech Human Touch philosophy. In everything we do, the well-being and future of our students and staff are paramount. From research and teaching to personnel management, campus management and the use of new technologies.

Our mission We are a university of technology

Our university is a public institution that serves society. We are accountable to society for the ways in which we use our academic freedom. We are responsible for ensuring that the power of science and technology is harnessed to achieve the best possible impact in a changing world. We cherish our rich tradition of combining technical and social sciences in our five profiling themes: Improving healthcare by personalized technologies; Creating intelligent manufacturing systems; Shaping our world with smart materials; Engineering our digital society; and Engineering for a resilient world.

Our mission We help to strengthen society

We help society meet the challenges of today and tomorrow. But we are also transparent about what science and technology can and cannot do in finding sustainable solutions. And help translate these solutions into everyday life.

Our mission We are sustainable

We want our communities to flourish and show resilience, so we seize opportunities for innovation. We are knowledgeable and have an eye for what society needs. Our students and staff receive all the guidance they need in their quest for ecological, social and economic sustainability. “The University of Twente is all about people. Our sustainable technologies help to strengthen society.”

Stay informed.

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Data Science

How to Become a Quantitative Analyst [+ Salary & Career Guide]

What is a Quantitative Analyst?

What does a quantitative analyst do (+ average day).

• Career Outlook
• Companies Hiring
• Paths to Careers
• Education Requirements

Quantitative analysts (often called “quants” for short) are described by Investopedia as “ the rocket scientists of Wall Street .” Currently in high demand thanks to their advanced skills in mathematics, finance, and technology, quantitative analysts typically command high salaries. How high?

Read on to learn more about:

• Quantitative analyst salary data
• Quantitative analyst job growth and career outlook
• Companies across industries hiring quantitative analysts
• Quantitative analyst hard skills and soft skills
• How to become a quant
• Quant career paths and educational requirements
• Answers to frequently asked questions

Quantitative analysts are professionals who specialize in “the design, development, and implementation of algorithms and mathematical or statistical models intended to solve complex financial problems,” according to the Corporate Finance Institute . “In their work, quantitative analysts apply a blend of techniques and knowledge from multiple disciplines including finance, economics, mathematics, statistics, and computer science.”

Investopedia defines quantitative analyst as: “A professional who uses quantitative methods to help companies make business and financial decisions. Investment banks, asset managers, hedge funds, private equity firms and insurance companies all employ quantitative analysts to help them identify profitable investment opportunities and manage risk.”

Quants are in particularly high demand in the world of investing and securities trading because of their ability to develop valuable insights intended to give their employers a competitive edge. “While the computer algorithm does the grunt work, it is quantitative analysts who are the brains behind these algorithms,” says Investopedia. “The best in the business make their employers millions of dollars on a monthly basis simply by programming algorithms that are fast and efficient enough to locate the best trades before the competition.”

For those with the right skills, education and experience, “a position as a quantitative analyst is financially lucrative and intellectually stimulating,” according to QuantStart . However, “the competition for roles is tough, especially within top tier funds and investment banks.”

Commonly employed by a broad spectrum of financial industry organizations (securities firms, commercial banks, investment banks, wealth management firms, hedge funds, etc.), quants are also employed by insurance companies, accounting firms, management consulting organizations, and financial software companies, among others.

Often their core responsibilities revolve around using advanced quantitative methods to scope out opportunities and evaluate risk. The work of a quantitative analyst is nearly always connected to quantitative research; according to Street of Walls , financial industry quants are typically focused on:

• Improving the trading architecture used to place trades
• Improving the signals used to evaluate trade ideas
• Reducing transaction costs and/or market impact
• Managing portfolio risk
• Testing and deploying new trading strategies

Street of Walls notes that daily activities may include:

• Reading academic literature to help ground trading strategies in theory
• Creating and testing a hypothesis for a given trading strategy
• Back-testing the strategy with existing data on an out-of-sample basis using actual transaction costs
• Programming and implementing the trading strategy
• Stress-testing the strategy to gauge environments in which the strategy may fail to perform
• Creating a risk-management framework for implementing the strategy and proposing a systematic method for increasing or decreasing portfolio risk

The work is often considered challenging to explain in layman’s terms. In this Investopedia video , three quantitative analysis professionals talk about the nature of their work, including one who needed to figure out a way to “explain what I do to my mother.”

Quantitative Analyst Career Outlook [Demand is High]

In the trading world, quantitative analysts are especially in demand. “As financial securities become increasingly complex, demand has grown steadily for professionals who not only understand the complex mathematical models that price these securities, but who are able to enhance them to generate profits and reduce risk,” reports Investopedia . Demand for talent driven by these and other factors:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies.

How Much Does a Quantitative Analyst Make?

Six-figure salaries and above are the norm for quantitative analysts. Salary figures tend to vary widely (and estimates from employment websites are often updated in real time), but reports of $100,000+ salaries are consistent across the board for quantitative analysts.

The U.S. Bureau of Labor Statistics reports the following average and upper-level salaries for financial and investment analysts, by sector:

• Securities, commodities, etc. – $125,040
• Investment pools and funds – $132,350
• Software publishing – $110,930
• Other information services – $110,150

Here are some additional examples:

• $135,846 ( Indeed )
• $117,000 ( Talent )
• $112,000 up to $174,000 ( Glassdoor )
• $106,751 ( Investopedia )

Compensation for highly skilled quants at hedge funds and other organizations may also include large bonuses, according to Street of Walls , which quotes a survey showing an average total compensation of $230,000.

Companies (By Industry) Hiring Quantitative Analysts

Anyone who is curious about how to become a quant will also be interested in taking a closer look at the vast array of big-name and lesser-known companies who are looking to hire quantitative analysts.

As mentioned above, a lot of the action is in the financial industry, but there are also career opportunities for people skilled at quantitative analysis in insurance, accounting, software, consulting, social media, and more. During a recent LinkedIn search, the NFL’s Philadelphia Eagles were seeking a “Quantitative Analyst – Football Operations.”

A recent LinkedIn search provides a look at some of the best-known organizations seeking quantitative analysts, including:

• Fidelity Investments
• Bank of America
• Goldman Sachs
• Magnetar Capital
• Penn Mutual
• Navy Federal Credit Union
• The Motley Fool
• Capital One
• And countless more

Quantitative Analyst Hard Skills

“In general, quantitative analysts use their mathematical and statistical skills to detect market changes,” says Study.com . “Advanced knowledge of calculus, engineering and game theory is key; programming skills are essential, particularly in C++. A strong background in modeling with large amounts of data is also mandatory.”

In terms of the key skills, knowledge, and aptitude needed to excel as a quantitative analyst, the bar is pretty high. Not all of these skills are needed for every job, but the following are some of the key skills and subject areas that help position quants for career success:

Investopedia emphasizes advanced understanding of mathematical computation, including:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Other sources make reference to:

• Strong abilities in data mining and data analysis
• Extensive financial knowledge
• Programming skills
• And, of course, software skills

Advanced software skills are “critical to job performance,” reports Investopedia. “C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS or a similar package. Pricing knowledge may also be embedded in trading tools created with Java, .NET or VBA, and are often integrated with Excel. Monte Carlo techniques are essential.”

Quantitative Analyst Soft Skills

As in many higher-level analyst and data science-related jobs, well-rounded soft skills are also essential, with many job listings emphasizing excellent written and verbal communication skills. This is because, in addition to generating high-value work product, analysts must be skilled at documenting and presenting their findings to others within their organization.

Other general, less technically specific skills include:

• Teamwork and collaboration
• Problem solving
• Proficiency at research
• Leadership capabilities

Paths to Become a Quantitative Analyst [Degree vs. Non-Degree]

There is some non-degree-oriented training you can do to enhance your chances for career success, but most quantitative analyst jobs require an advanced degree and in some cases a Ph.D. Examples of industry certifications pursued by some professionals in this field include a Chartered Financial Analyst (CFA) designation or a Certificate in Quantitative Finance (CQF).

Education Requirements for a Quantitative Analyst

When hiring quants, “most firms look for at least a master’s degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master’s degrees in financial engineering or computational finance are also effective entry points for quant careers,” says Investopedia. Other master’s degrees that connect to the field of quantitative analysis include:

• Data science
• Mathematical finance
• Financial engineering

Because of the strong emphasis on working with data and using advanced skills to uncover actionable insights, an advanced degree in data science can be an extremely helpful stepping stone.

The Corporate Finance Institute reports, “The rapid introduction of data science and machine learning in finance [has] increased the demand for candidates with the relevant educational background in said fields.” And Wall Street Mojo cites data science as one of four key areas of expertise, saying, “To be able to thrive in this career, you need to be amazing at four specific subjects – mathematics, data science or software, finance, and application development.”

Quantitative Analyst vs. Other Similar Job Titles

Here are some additional job titles in quantitative analysis and related fields:  

• Quantitative Trader
• Quantitative Researcher
• Quantitative Developer
• Financial Engineer
• Investment Analyst
• Risk Analyst
• Quantitative Portfolio Manager
• Data Analyst (Finance)
• Quantitative Software Engineer
• Quantitative Investment Manager
• Manager, Data Science and Analytics
• Data Scientist, Consumer Analytics

Quantitative Analyst vs. Data Scientist [Similarities and Differences]

“Quants and data scientists have more in common than you might think. The differences between them are shrinking as tech plays a more prominent role in finance.”

That’s the intro to a report on the similarities and differences between these two roles that rely on advanced analytical skills to derive valuable business insights from complex sets of data.

One of the biggest differences is that skilled data scientists have perhaps a broader horizon of potential career pathways, since quants are employed largely by sectors dealing in high finance and risk analysis.

The report mentioned above also cites noted finance columnist Matt Levine arguing that “there is no hard-and-fast dividing line between quants and data scientists, and that eventually, the baseline expectation for quantitative analysts will be fundamentally the same as those for data scientists.”

In a 2018 Bloomberg column titled “Quants Might Not Be So Special Someday: In the Future, Everyone Will Be a Data Scientist,” Levine envisions a future in which “every reasonably sophisticated investment firm will have a data science department.” Learn more about the Data Scientist vs. Quantitative Analyst debate in these articles by Springboard and Noodle.com .

A data science master’s degree, such as the online Master of Science in Applied Data Science , will typically offer a solid foundation in technical and soft skills that are also extremely valuable to aspiring quants, including the widely used open-source programming languages Python and R.

FAQs About Quantitative Analyst Careers

Q: What is the average salary for a quantitative analyst?

A: Salaries for quantitative analysts often range well above $100,000. Employment website Indeed cites an average salary for a quantitative analyst in the U.S. of $134,000.

Q: What are the key differences between quantitative analysis and data science?

A: One of the biggest differences is that quantitative analysts are highly sought after in the finance sector, while data scientists are in demand across a far broader range of industries, including financial services, technology, cybersecurity, media, health care, retail, manufacturing, and more.

Q: Would a master’s in data science help me pursue a career as a quant?

A: Yes. A strong data science master’s degree engages graduate students in many of the same technical and soft skills that are also extremely valuable to aspiring quantitative analysts.

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Quants: The Rocket Scientists of Wall Street

Getty Images, Prapass Pulsub

Quantitative analysts are professionals who understand the complex mathematical models that price financial securities and are able to enhance them to generate profits and reduce risk. As financial securities have become increasingly complex, demand has grown steadily for quantitative analysts , often called simply "quants," or even the colloquially affectionate "quant geeks."

Because of the challenging nature of the work—which needs to blend mathematics, finance, and computer skills effectively—quant analysts are in great demand and able to command very high salaries. Here's a look at what they do, where they work, how much they earn, and what knowledge is required, to help you decide whether this may be the career for you.  

Key Takeaways

• Quantitative analysts, or quants, combine their skills in finance, math, and computer software to analyze and predict markets, creating complex models that can be used to price and trade securities.
• They tend to work in investment banks and hedge funds, although insurance companies, commercial banks, and financial software and information providers may also hire them.
• Quants work in major financial centers in the U.S. and in London and Asia, among other places across the globe.
• Firms often look for candidates who have a master's degree or a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics.
• Compensation can be in the low-to-middle six figures.

What Do Quantitative Analysts Do?

Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.

Quants who work directly with traders, providing them with pricing or trading tools, are often referred to as " front-office " quants.

In the " back office ," quants validate the models, conduct research, and create new trading strategies. For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.

The high demand for quants is driven by multiple trends:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies 

Quantitative analyst positions are found almost exclusively in major financial centers with trading operations. In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts, and Stamford, Connecticut.

Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant's brainpower and abilities.

But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one. 

What Do Quants Earn?

Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend. It is not uncommon to find positions with posted salaries of $200,000 or more, and when you add in bonuses, a quant could earn over $300,000 per year.

As with most careers, the key to landing high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities. 

The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm's earnings, also known as profit and loss (P&L) .

At the other end of the pay scale, an entry-level quant position may earn only $120,000 to $210,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.

Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that's about as high as the compensation package generally would go.

Despite the high pay level, some quants do complain that they are "second-class citizens" on Wall Street and don't earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.

The estimated median total pay of a quantitative analyst in the U.S. in 2024. Google is among the highest-paying companies for a quant, offering a median annual pay of $262,000.

Quants Skills and Education

Financial knowledge.

Many financial securities, such as options and convertibles , are easy to understand conceptually but are very difficult to model precisely. Because of this hidden complexity, the skills most valued in a quant are those related to mathematics and computation rather than finance.

It is a quant's ability to structure a complex problem that makes them valuable, not their specific knowledge of a company or market.

A quant should understand the following mathematical concepts:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Key financial topics include:

• Portfolio theory
• Equity and interest rate derivatives, including exotics
• Credit-risk products

Some quants will specialize in specific products, such as commodities, foreign exchange (Forex), or asset-backed securities .

Computer Competency

Software skills are also critical to job performance. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS, or a similar package.

Pricing knowledge may also be embedded in trading tools created with Java, .NET, or VBA , and are often integrated with Excel. Monte Carlo techniques are essential. A majority of the work is also realized in Python, as scripting-type languages are good for running lots of data and multiple scenarios.

Education and Certifications

Most firms look for at least a master's degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master's degrees in financial engineering or computational finance are also effective entry points for quant careers.

Generally, an MBA is not enough by itself to obtain a quant position, unless the applicant also has a very strong mathematical or computational skill set in addition to some solid experience in the real world. 

While most financial certifications, such as the Chartered Financial Analyst (CFA) designation likely wouldn't add much value to a prospective quant's resume, one that may is the Certificate in Quantitative Finance (CQF) —which you may earn globally via distance learning in a six-month intensive program.

Clearly, you need to have "the right stuff" to be a quantitative analyst. It requires both the intellectual ability to master complex and abstract mathematical domains and a willingness to tackle challenges that can seem insurmountable—all while under considerable pressure—which only a select few can do.

But that also doesn't mean that everyone who has the ability to be a quant should become one. The financial problems that quants face are very abstract and narrow. Unlike fundamental or qualitative analysts , quants don't read annual reports, meet with management, visit operations, prepare roadshows, or talk to shareholders. Most of their time is spent working with computer code and numbers on a screen.

Individuals with strong analytical skills are valuable in many different areas of finance, such as economic and financial analysis, for example. Having to compete against the best and brightest quants every single day may not be the quickest path through the ranks, especially for those with broader skills and interests and a desire to manage.

Another career issue to consider is that many Ph.D. quants who come from academic environments find they miss the research environment. Instead of being able to study a problem for several months, when supporting a trading desk you need to find solutions in days or hours. This usually precludes making any breakthroughs in the field. 

Do Quants Get Paid Well?

Yes, quants tend to command high salaries, in part because they are in demand. Hedge funds and other trading firms generally offer the highest compensation. Entry-level positions may earn only $120,000 to $210,000, but there is usually room for future growth in both responsibilities and salary and the ability to earn upwards of $300,000.

How Hard Is Quant Finance?

It takes advanced-level skills in finance, math, and computer programming to get into quantitative trading, and the competition for a first job can be fierce. Once someone has landed a job, it then requires long working hours, innovation, and comfort with risk to succeed.

Do You Need a Ph.D. to Be a Quant?

Having a Ph.D. in a subject like math, finance, economics, or statistics can be a definite plus for anyone wanting to become a quant. However, a master's degree in computational finance or financial engineering can also be the ticket to a career as a quantitative analyst.

Success in quantitative analysis is largely based on knowledge, talent, merit, and dedication instead of the ability to sell, network, or play politics. The quants who work in the field are there because they can do the job well—an environment that many find remarkably refreshing—and they are justly rewarded for their work.

Bureau of Labor Statistics. " Financial Analysts ."

Glassdoor. " Quantitative Analyst Salaries ."

Bureau of Labor Statistics. " Financial Analysts: Pay ."

Glassdoor. " How Much Does a Junior Quantitative Analyst Make? "

Glassdoor. " How Much Does a Quantitative Analyst Make? "

Bureau of Labor Statistics. " Financial Analysts: How to Become One ."

Certificate in Quantitative Finance. " Who Is It For? "

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2025 Summer Internship Quantitative Analytics Program (PhD) - Early Careers

• Administrative & Corporate Services
• 08 Aug 2024

About this role:

Wells Fargo is seeking talent to join the Summer Internship Quantitative Analytics Program (PhD) – Early Careers.  Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview 

The internship program is a ten-week overview of Wells Fargo, and prepares you to develop, implement, calibrate, or validate quantitative models. The program is designed to provide you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Based on individual achievement, those who excel in the internship program may receive an offer for full-time employment. 

There are two tracks:   

·         The Capital Markets track deals with mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX, Rate, Mortgages, and Asset-Backed Finance.  

·         The Risk Analytics & Decision Science track deals with statistical, econometric, and machine learning/AI models for a variety of applications, including loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and analysis of unstructured data such as text and audio.   

Tracks will be designated and assigned to the candidate upon reviewing application. 

 Placement group includes:  

·         Artificial Intelligence Machine Learning Model Development Center 

·         Traded Products Model Development  

·         Risk Modeling Group – Forecast Model Development 

·         Risk Modeling Group – Decision Model Development 

·         Market and Counterparty Risk Analytics  

·         Mortgage Model Development Center 

·         Model Risk Management  

·         Consumer Model Development Center 

In this role, you will:

·         Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages 

·         Be part of a team that provides solutions to business needs and improves risk management processes 

·         Communicate solutions to stakeholders through verbal presentations and written documentation 

·         Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities 

What you will experience:  

·         Spend your Intern Induction Week at an offsite location 

·         Structured and engaging onboarding experience  

·         Speaker series with Wells Fargo senior leaders 

·         Professional development opportunities 

·         Networking and engaging with peers 

·         On-the-job experiences contributing to strategic business goals 

Ideal candidate for this role:

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience  

For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics  

For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing 

• Energetic self--starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth.

Program Dates: June - August,2025 (10 weeks, 40 hours per week) 

Program Location:  Charlotte, NC 

Required Qualifications:

6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

Currently pursuing a PhD degree with an expected graduation date between December 2025 – or later 

Currently pursuing a PhD degree in with emphasis on Statistics, Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field  

Experience in computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++  

Excellent verbal, written, and interpersonal communication skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

#earlycareers

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

Posting End Date:  

*Job posting may come down early due to volume of applicants.

We Value Diversity

At Wells Fargo, we believe in diversity, equity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in US: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Applicants with Disabilities

To request a medical accommodation during the application or interview process, visit  Disability Inclusion at Wells Fargo .

Drug and Alcohol Policy

Wells Fargo maintains a drug free workplace.  Please see our Drug and Alcohol Policy to learn more.

Wells Fargo Recruitment and Hiring Requirements:

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.

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Any tools at the following website are provided for educational and illustrative purposes only. Wells Fargo does not guarantee the accuracy of the calculations and their applicability to your circumstances.

The Darden Ph.D. Program

Ph.d. - data analytics and decision sciences, data analytics and decision sciences.

Research in Data Analytics and Decision Sciences is focused on mathematical methodology with business applications and includes:

• Dynamic programming
• Game theory
• Mechanism design
• Machine learning
• Optimization
• Search theory
• Wisdom of crowds
• Forecasting
• Decision analysis
• Pricing and revenue management
• Behavioral models of human decision making

Students will be required to take a few core courses but also have the freedom to choose from the many Ph.D. courses that the UVA campus offers, and are encouraged to follow their interests and expand their knowledge. Whatever portfolio they choose, students are expected to be strong in several areas of fundamental theory (e.g., real analysis, probability theory, micro-economics, game theory, decision theory, optimization) as well as methods (e.g., statistics, econometrics, experimental design, coding), so that they can begin exploring novel research questions by the middle of their second year. There is no requirement for prior experience in business applications. Students with a strong quantitative background can be taught all the necessary skills for success in academic research.

Michael Albert

Assistant professor of business administration; areas of expertise: mechanism design, machine learning.

Saed Alizamir

Associate professor of business administration.

Joe Andrasko

Professor of practice.

Manel Baucells

David m. lacross associate professor of business administration; areas of expertise: prescriptive and descriptive decision theory, behavioral models of human decision making.

Assistant Professor of Business Administration; Areas of Expertise: Data-driven Optimization, Machine Learning, Pricing

Robert L. Carraway

Yiorgos allayannis distinguished associate professor of business administration.

Academic General Faculty

Rupert Freeman

Assistant professor of business administration; areas of expertise: computational social choice, fair division, resource allocation, prediction mechanisms.

Yael Grushka-Cockayne

Professor of business administration; areas of expertise: forecasting, forecast aggregation, project management, data science for business.

Dana Popescu

Associate professor of business administration; areas of expertise: pricing optimization, revenue management and demand forecasting.

Assistant Professor of Business Administration; Areas of Expertise: Dynamic Models of Decision-Making Over Time, Search Theory, Game Theory, Incentive Design, Healthcare Applications

Understanding How to Become a Quantitative Analyst

A position as a quantitative analyst is financially lucrative and intellectually stimulating. The competition for roles is tough, especially within top tier funds and investment banks. Being top of the class and gaining a great degree is not enough these days, especially after the market downturn following the 2008 credit crisis. What attributes should a good prospective quant have and how can you obtain that lucrative job as a financial engineer?

A quantitative analyst can have a varying set of responsibilities. Topics such as systematic trading, financial research, managing risk, options pricing and quantitative programming are all roles that might be termed a "quant". At the outset, you must evaluate your skills and determine what you are good at, be it mathematics, data science or software development. This will put you in a much better frame when applying for particular quant jobs. There has been a transition recently away from investment bank hiring towards hedge fund/quantitative trading positions. Thus, options pricing is decreasingly being sought, whereas statistics and pattern recognition are now being requested.

If you wish to work in a quantitative role, it is best to have gone down one of three paths. The most likely way into a quant job is to obtain a PhD in a mathematical discipline such as Physics, Engineering or CompSci. Clearly mathematical finance is a good area of research, but probability, stochastic calculus, statistical analysis and machine learning are all highly valued. The main benefit of a PhD course is that the firm will understand your capability towards independent research, without the need for being excessively "micro-managed". These skills are most useful in the top tier funds, where a research atmosphere is often cultivated.

Another path into financial engineering is via a Masters course in Mathematical Finance. These are usually known as MFEs of MMFs. Individuals who possess natural mathematical ability, but lack financial knowledge, are particularly good candidates for these courses. If you wish to have a career transition, they also make a perfect spring board. The task of a financial masters course is to educate the student in options pricing, probability theory, financial regulation and sophisticated software development (almost certainly C++). The lecturers will provide a beneficial network, upon which to cultivate a potential career and this alone can justify the $50,000 course price tag.

If you are more computationally inclined, then a quantitative development role will likely be more fitting. Firms are always on the look out for those with advanced object-oriented experience, particularly in C++, Python or Java. As a "quant dev" you will work closely with the modellers to take a prototype (often written in MatLab/R), optimise it and make it highly fault tolerant for a production environment. However, the roles vary significantly in what is asked for. An investment bank will want experience linking together legacy systems and large libraries, perhaps to produce interfaces for the traders in Excel. A hedge fund will likely be interesting in statistical coding - such as machine learning or pattern recognition. Be aware that development talent is often the deciding issue in quantitative interviews. Hence, the more practice and experience you can gain prior to applying, in a solid object-oriented language, the better.

Join the QSAlpha research platform that helps fill your strategy research pipeline, diversifies your portfolio and improves your risk-adjusted returns for increased profitability.

The Quantcademy

Join the Quantcademy membership portal that caters to the rapidly-growing retail quant trader community and learn how to increase your strategy profitability.

Successful Algorithmic Trading

How to find new trading strategy ideas and objectively assess them for your portfolio using a Python-based backtesting engine.

Advanced Algorithmic Trading

How to implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with R and Python.

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Spark Investment Management (Spark)

Phd-level quantitative trader.

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Job Description

• are good with computers but don’t want to be a pure programmer;
• have the real-world skills to work in different areas and combine these inputs into focused projects and working hypotheses;
• and have exceptional quantitative skills,

then you are a rare breed. You can polish all your fundamental skills and develop even further at Spark while achieving more financial independence than in other opportunities.

As Quantitative Trader, you will work toward implementing quantitative finance solutions.  You will earn outstanding compensation, build your skill base, and tackle the variety of interesting and difficult projects our entrepreneurial environment has to offer.

You will develop real-world expertise in trading to leverage your skills.  The Quantitative Trader will use quantitative skills in a small-team environment while starting at a higher level than in a purely analytical role.  Your work will include complicated computer assignments and statistical analysis but also the next steps toward implementing your analysis and conclusions.  A background in machine learning, optimization, or statistical modeling will be helpful but can also be picked up on the job if you have a strong computer and quantitative base.

As the quant trading sector matures, the difficulty of inventing strategies increases.  In this role, you will have the opportunity to generate more revenue from already-profitable strategies without having to first invent them. If you can bring high-level technical skills and a strong work ethic, along with a desire to improve your general skills, we can help you succeed. 

Job Qualifications

This position requires good computer skills, strong communication skills to work with people in different areas, and exceptional quantitative skills.  A high level of skill in software can compensate for deficits in mathematical knowledge and ability.

Because you will be involved with trading and we value your quantitative and computer skills as well as your other multifaceted abilities, this opportunity is superior both in starting compensation and long-term potential.  This position requires a PhD, and your future career path can specialize or broaden within Spark depending on your inclinations.  When you submit your resume, please include your GPAs and standardized test scores.  If you have awards or medals in mathematical or scientific competitions, be sure to include those in your application materials.  If you are facing a pressing deadline due to other offers, you can contact [email protected] to request consideration before our on-campus date. 

Company Description

The next generation in hedge fund management.  For over a decade, our outstanding people, many of whom have PhDs, have been developing the next generation of technologies, ideas, and strategies.  We have been highly successful because of both our technically superior strategies and our brilliant people.  We have a close-knit group, distinguished by their interest in our shared success, half of whom are graduates from Harvard, MIT, and Cornell.  This lucrative synthesis of human and machine cognition in an intellectually rich, supportive environment is at the core of Spark.  We have experience with H-1B and green card sponsorship, and visa status is not a constraint.  All submissions kept confidential.

www.sparkim.com

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Quantitative Research Intern, PhD

As a Quantitative Research Intern, you will work side-by-side with our Research Team of mathematicians, scientists and technologists, to develop and enhance the models that drive Optiver’s trading. You will tackle a practical research challenge that has real-world impact and directly influences Optiver’s trading decisions. In our business, where the markets are always evolving, you will use your skills to predict its movements.

What you’ll do:

Led by our in-house education team, you will delve into trading fundamentals and engage in a research project that makes a real-world impact. You will be paired with one of Optiver’s seasoned researchers, providing you exposure to a variety of research areas, including:

• Using statistical models and machine learning to develop trading algorithms.
• Leveraging big data technologies to analyze high-frequency trading strategies, market microstructure, and financial instruments to identify trading opportunities.
• Building stochastic models to determine the fair value of financial derivatives.
• Combining quantitative analysis and high-performance implementation to ensure the efficiency and accuracy of pricing engines and libraries.

Based on your performance during the internship, you could receive an offer to join our firm full-time after your studies or return as an intern the following summer, depending on when you graduate.

What you’ll get:

You’ll join a culture of collaboration and excellence where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a high-performing environment alongside talented colleagues working collectively to tackle the toughest challenges in the financial markets.

Additionally, you’ll receive:

• A highly-competitive internship compensation package
• Optiver-covered flights, living accommodations and commuting stipends (where applicable)
• Office perks, including fully paid commuting expenses, daily breakfast and lunch, social events and clubs

Who you are:

• Currently enrolled in a PhD program in Mathematics, Statistics, Computer Science, Physics or a related STEM field with outstanding academic performance
• Anticipated graduation date between  December 2025 and June 2027
• Availability to commence full-time employment upon graduation in 2026 or 2027
• Must be available to intern during Summer 2025
• Solid foundation in mathematics, probability, and statistics
• Excellent research, analytical, and modeling skills
• Independent research experience
• Proficiency in any programming language
• Knowledge of machine learning, time-series analysis, pattern recognition, and NLP is a plus
• Strong interest in working in a fast-paced, collaborative environment
• Fluent in English with strong written and verbal communication skills

Diversity statement

Optiver is committed to diversity and inclusion .

For answers to some of our most frequently asked questions, refer to our Campus FAQs .

Optiver is supportive of US immigration sponsorship for this role.

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2025.

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Wells Fargo

2025 quantitative analytics development program – capital markets (phd).

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About this role:

Wells Fargo is seeking talent to join the Quantitative Analytics Program. Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview  

The Quantitative Analytics Program provides you with an overview of Wells Fargo and prepares you to develop, implement, calibrate, or validate quantitative models. It is designed to give you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Upon completion of two six-month rotations, you will join a team within your program track. 

The Capital Markets track deals with mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX, Rate, Mortgages, and Asset-Backed Finance.   

Placement group includes:   

Traded Products Model Development  

Market and Counterparty Risk Analytics  

Mortgage Model Development Center  

Model Risk Management  

Consumer Model Development Center  

In this role you will:  

Be part of a team that provides solutions to business needs and improves risk management processes  

Communicate solutions to stakeholders through presentations and written documentation  

Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.  

  Ideal candidate for this role:  

Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Java, Spark, and C++  

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience.  

Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth. 

Program Start Date:  July 2025   

Program Location:   Charlotte, NC  

Program Length: 24 months (Year 1: training and 2 rotations; Year 2: permanent placement)  

Required Qualifications:

2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Master’s degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:

​Currently pursuing PhD degree with an expected graduation date in December 2024-June 2025 OR graduated from a PhD program after May 1, 2022 and is currently completing a postdoc with emphasis in Mathematics, Operations Research, Physics, Engineering, Computer Science, Statistics, Quantitative Finance or related quantitative field.   

Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages  

Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics   

Excellent verbal, written, and interpersonal communication skills 

Ability to work effectively, as well as independently, in a collaborative, change driven environment 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement  

#earlycareers 

How to Apply: 

The 2025 Quantitative Analytics Development Program will be accepting applications through the Wells Fargo Career website.  

To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.  

Search by Job ID #: R-3865666 or by keywords, “2025 Quantitative Analytics Development Program – Capital Markets – PhD” and follow the instructions to complete your application. 

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

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PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM)

The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early diagnosis, and therapy of diseases like cardiovascular diseases, autoimmune diseases or cancer. Overall, the institute strives to advance precision medicine by combining knowledge from different fields such as biology, chemistry, pharmacology, physics, and computer science. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in Dortmund we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) A new project area in the institute is the development of artificial intelligence (AI) software for biomedical image analysis and building tools for AI-based software quality assurance. You will be responsible for • Develop new machine learning algorithms for microscopy image analysis problems, especially intracellular structure segmentations in 2D/3D/4D microscopy images., which are driven by real applications in life science research • Develop solutions to integrate large foundation models into microscopy image analysis and analytical data analysis workflows, together with other team members • Implement AI-based microscopy image analysis software as python packages and napari plugins • Develop unit-testing tools for bioimaging AI software • Report findings and methods in conference and journal papers • Conduct software demo and tutorials at national and international workshops Your profile • Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science/biomedical engineering or of relevant scientific field • A solid background in machine learning • Extensive experience with either computer vision or image analysis • Good knowledge of deep learning packages, PyTorch • Familiar with foundation models (vision large models or multi-modal large language models) • Familiar with continuous integration, continuous development, and Github Actions • Good presentation and writing skills • Proactive, independent, and solution-oriented way of working • Fluent English (spoken and written) • Publications at top-tier computer vision conference or journals is a plus • Experience with open-source software development is a plus We offer • A clear research topic as well as multifold training and support for PhD students in framework of ISAS and the Leibniz association • Training and scientific development opportunities in an international environment and an excellent working atmosphere in a very dynamic and professional team • Extensive state-of-the-art equipment and infrastructure in various analytical methods • The opportunity to present your data on international conferences and participate in workshops Non-residents who apply for this job will receive help from the institute to find accommodation and to handle authorities. Applications from disabled applicants are welcome. ISAS supports the principle of equal opportunity for all employees and therefore particularly encourages women to apply. The salary will be according to the German TV-L. The full-time position is offered for 3,5 years. The position is available immediately. ISAS collects and processes the personal data of its applicants in accordance with European and German legal regulations. Further information on data protection and the processing of personal data can be found at www.isas.de/datenschutzhinweise-fuer-bewerberinnen-und-bewerber. The closing date for applications is September 21, 2024. Please apply via our applicant portal . Informal requests (reference number 314_2024) are welcome and can be directed to the Human Resources team ([email protected]). Further information about the institute can be found at: http://www.isas.de/en.

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Senior Quantitative Finance Analyst

Job Description:

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Job Description: Enterprise Model Risk Management seeks a Senior Quantitative Finance Analyst to conduct independent review and testing of complex models based on artificial intelligence (AI) and machine learning (ML) techniques including natural language processing (NLP). These are high profile modelling areas in the bank, with continual senior management and regulatory focus. The Senior Quantitative Finance Analyst will be a key leader in Model Risk Management. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in applying AI/ML models to manage the bank's large footprint in various line of businesses. The AI/ML model inventory cover areas of natural language processing, deep learning and ensemble learning. The model inventory is rapidly increasing especially with generative AI and large language models (LLMs). The position will be responsible for: • Performing model review activities including but not limited to independent model validation/challenge, annual model review, ongoing monitoring report review, required action item review, and peer review. • Conducting governance activities such as model identification, model approval and breach remediation reviews to manage model risk. • Providing hands-on leadership for projects pertaining to statistical modeling and AI/ML approaches; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects. • Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating, and interacting with the third line of defense (e.g., internal audit) as well as external regulators. • Writing technical reports for distribution and presentation to model developers, senior management, audit, and banking regulators. • Acts as a senior leader and Subject Matter Expert (SME) to help management’s decision making and guide junior team members.

Minimum Education Requirement: Master’s degree in related field or equivalent work experience

Required Qualifications & Skills: • PhD or Masters in a quantitative field such as Mathematics, Physics, Engineering, Computer Science or Statistics. • Solid 3+ years of work experience at another financial service or technology firm in AI/ML, quantitative research, model development, and/or model validation. • Expert of AI/ML methodologies including NLP, e.g., methods in NLP used for text-to-text, speech-to-text/text-to-speech tasks. Familiarity with techniques in generative AI and LLMs. • Proficient in Python, and ideally experienced in AI/ML packages, e.g., scikit-learn, TensorFlow, XGBoost, PyTorch, spaCy. • Domain knowledge such as retail banking, technology, operations, financial market is a plus, including the use of analytics to perform automatic speech recognition, fraud, cyber risk monitoring, retail customer digital experience, ATM operations, and/or cash management. • Strong knowledge of financial, mathematical, and statistical theories and practices, and a deep understanding of modeling process, model performance measures, and model risk of AI/ML models. • Understanding of additional risks of AI/ML models in areas such as privacy and information security will be a plus. • Strong written and verbal communication skills and collaboration skills. This role involves communicating with various groups within the firm including stakeholders with non-technical background. • Critical thinking and ability to independently and proactively identify/suggest/resolve issues. • Motivated to continuously research and share state-of-the-art technologies, methodologies, and applications in the AI/ML field.

Hours Per Week:

Weekly Schedule:

Referral Bonus Amount:

Hours Per Week: 

Learn more about this role

JR-24034310

Manages People: No

Travel: Yes, 5% of the time

Jersey City pay range:

$125,000 - $210,000 annualized salary, offers to be determined based on experience, education and skill set.

Discretionary incentive eligible

This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.

This role is currently benefits eligible . We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.

New York pay range:

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Data Science Analyst- On-Site

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This position will be on-site on the Jacksonville Florida Mayo Clinic Campus.

Data Scientists at Mayo Clinic perform detailed analysis of large bodies of heterogeneous data in order to discover new patterns and insights having an impact upon patient health and augmenting human capabilities. Candidate has expertise in AI, machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory and data science methods used to analyze data. May work with knowledge architects, informaticians and clinicians at Mayo, and partner outside companies to develop and deploy applications to bring AI and analytic solutions to nontechnical users, often at the point of care. Applies and modifies existing scripts or software applications to support data management, data extraction, data analysis, and AI as required May develop predictive models on datasets to address various business problems by leveraging statistical modeling, operations research, machine learning, or data mining techniques. May provide Consultative Services to departments/divisions and/or support scientific projects under the guidance of a designated senior level data scientist. Other responsibilities: •Provides data insights for business problems that can be approached with analytics techniques to collect, explore, and extract insights from structured and unstructured data. •Has expertise in the data science methods used to analyze data, and knowledge of data types, topics, and scientific challenges and approaches. •Executes analytical procedures in the framework of a specific project work request. •Creates or modifies scripts or software applications to support data management, data extraction and data analysis as required. •Contributes to the interpretation of data analysis and to writing reports. •Helps customers understand the data set and provide training and suggestions for improvement on the data request. •Leverages communication and interpersonal skills and works with subject matter experts •Presents findings in easy to understand terms for the business or clinical practice.

A Bachelor’s degree in a relevant field such as engineering, mathematics, computer science, health science, or other analytical/quantitative and a minimum of three years of professional or research experience in data science will be considered.

The preferred candidate will possess a Master’s degree or a PhD in a relevant field such as engineering, mathematics, computer science, health science, or other analytical/quantitative field and a minimum of one year of professional or research experience in data. Demonstrated ability to develop predictive models to address various business problems through leveraging advanced statistical modeling, machine learning, or data mining techniques. May provide consultative services to departments/divisions and committees. Demonstrated application of several problem-solving methodologies, planning techniques, continuous improvement methods, and analytical tools and methodologies (e.g. machine learning, statistical packages, modeling, etc.) required. Incumbent must have ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes. Interpersonal skills and time management skills are required. Requires strong analytical skills and the ability to identify and recommend solutions, advanced computer application skills and a commitment to customer service. Experience with data modeling and date exploration tools.

Preferred Qualifications 

The preferred qualifications are a guide to the skills and experiences that would be beneficial for the work you will engage in. We do not expect you to possess every qualification listed, and we encourage you to apply if you meet a subset of the following criteria: 

• Knowledge and experience in multiple programming and domain-specific languages (we primarily use Python, R, SQL, and Linux/Unix scripting) 
• Practical experience with reproducible research and version control systems like git, ensuring the maintainability and scalability of code bases 
• Experience with deep learning frameworks (e.g., PyTorch or TensorFlow) to implement various model types (e.g., CNNs, LSTMs, RNNs, object detection models) 
• Desire to bring new ideas to the team, the ability to work independently and continue learning in the rapidly evolving field of AI 
• Excellent communication skills, including technical writing and formal presentations 
• Experience in processing and analyzing/modeling time series, waveform, and image data 
• Familiarity with cloud computing ecosystems and their foundational technologies (e.g., virtualization, Docker, storage solutions, horizontal scaling) 
• Experience in developing proof of concept APIs/Applications utilizing Python or R frameworks (e.g., Dash, Streamlit, FastAPI, Flask, R Shiny, and Plumber) 
• Basic understanding of traditional statistics (e.g., experimental design, regressions, significance tests, confidence intervals, p-values)  

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Fuzzy Logic-Based Quantitative Development Model for Job Satisfaction in College Graduates

• Research Article
• Open access
• Published: 02 September 2024
• Volume 17 , article number  226 , ( 2024 )

Cite this article

You have full access to this open access article

• Yuqi Cheng 1  

College graduates’ job satisfaction is validated via their feedback, experiences and personal developments during their career progression. Validation is accomplished to ensure the students’ job satisfaction and retain them for a prolonged time. The traditional validation models have difficulties in analyzing the individual satisfaction level. The research issue is addressed by introducing the Quantitative Assessment Method (QAM) using Fuzzy Logic to validate the job satisfaction level of different newly placed students. The QAM approach assesses the student experience and personal development across various quarters. The fuzzy optimization uses two factors differentially using partial derivatives. The partial derivatives are extracted using the min–max functions of the fuzzification process such that the derivatives are halted after the maximum factors. The proposed method optimizes the validation using individual satisfaction levels and cumulative experience shared by the students. The available derivatives identify the best-afford job satisfaction level for different progression levels. This best-fit feature is handled using multiple min and max derivatives to extract optimal outputs. This proposed method is valid for improving satisfaction levels and experience analysis.

Explore related subjects

• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

Job satisfaction is defined as the range of contentment that employees feel about their jobs. Job satisfaction also provides happiness and positive energy, motivating them to improve their performance [ 1 ]. Job search methods and satisfaction practices are provided to college students during the learning period. The employee’s job satisfaction is examined based on job offers, satisfaction, positivity, performance, and salary rate [ 2 ]. The exact satisfaction level is evaluated based on the factors that improve job significance. Many techniques are provided to students during college [ 3 ]. Learning disabilities and functionalities are identified using job offers and performance. Various job fair methods are provided to college graduates [ 4 ]. Professional educational courses, practices, and vocational courses are available in college. The vocational courses provide feasible education and training sessions to the students, which improves the employee’s overall satisfaction range [ 5 , 6 ]. Profit and non-profit-based training sessions are also provided to college graduates. The profitable benefits are calculated based on functionalities that minimize the complexity of the job search process [ 7 ].

Quantitative development is a process that develops a particular area or field. Quantitative development is used for job satisfaction. The main goal of the quantitative approach is to analyze the exact relationship between employees and jobs in an organization [ 8 ]. The quantitative approach predicts the actual satisfaction level based on the performance range of the employee. An integrated model is used for job satisfaction improvement [ 9 ]. The integrated model uses a meta-analysis technique to evaluate the workers’ satisfaction range. The meta-analysis minimizes the computation process’s time and energy consumption ratios [ 10 ]. The integrated model provides optimal information for the quantitative development process, which enhances the systems’ overall performance range. A quantitative approach is also used for job satisfaction [ 11 ]. The quantitative approach predicts the relationship between performance and work. The expected information produces the relevant data for development and improvement processes. The quantitative approach increases the efficiency and feasibility range of the job satisfaction process [ 12 , 13 ].

The fuzzy logic approach is used for the job satisfaction evaluation process. The fuzzy logic approach uses fuzzy sets, which contain appropriate information for the evaluation process [ 14 ]. The fuzzy logic approach uses a set theory that measures the exact job satisfaction range of the workers. The performance range of employees, active ratio, and preference level are evaluated using set theory [ 15 ]. The attributes contain optimal key values for the job satisfaction measurement process. The set theory-based fuzzy logic approach increases the accuracy of the satisfaction detection process [ 16 ]. A fuzzy logic-based satisfaction detection method is also used in organizations. The exact satisfactory range of workers is identified from the database, reducing further processes’ latency [ 17 ]. The workers’ satisfaction range is calculated, providing optimal information for improvement and development. The fuzzy logic-based method increases employees’ overall performance range, enhancing the organization’s feasibility ratio [ 11 , 18 ]. A fuzzy logic-based quantitative approach is also used to improve job satisfaction. The quantitative approach analyzes the exact quality of jobs that are performed by the workers [ 19 ]. The traditional analysis has difficulties exploring the individual satisfaction level at different quarters. The problems are overcome by applying the Quantitative Assessment Method (QAM) with Fuzzy logic. The approach uses the fuzzy derivatives to identify the experiences and best affords definitive outputs. According to the discussions, the study’s contribution is listed as follows:

1.1 Contributions

Propose and discuss the process of developing a quantitative assessment model for evaluating the job satisfaction of college students using multi-variate data.

Analyzing the variations based on independent data attributes using fuzzy derivatives for experience extractions and best-afford definitive outputs.

Incorporating an ideal data source for analyzing the fuzzy impact on satisfaction and personal development at various professional levels.

Performing a self-analysis for different metrics by varying a certain factor to identify its impact on the assessment.

2 Related Works

Jiang et al. [ 20 ] developed a career-oriented satisfaction architecture for information technology (IT) professionals. The main aim of the architecture is to identify the exact career satisfaction range of IT professionals. The developed architecture tests the model based on job demands, managerial demands, and the satisfaction ratio of employees. The developed model provides IT professionals with proper career paths and career demands to, minimizing the complexity of the field.

Storey et al. [ 21 ] presented a job satisfaction theory for software developers. The proposed theory detects the impact of software developers’ productivity and career satisfaction levels. The proposed theory identifies both social and technical factors and features of developers. The identified features produce optimal information for the satisfaction detection process. The proposed theory increases the career growth range of software developers.

Hayat and Afshari [ 22 ] proposed a mediation model for job satisfaction detection. The proposed model is mainly used to detect the impacts of corporate social responsibility (CSR) among employees. CSR contains exact employee details, producing feasible data for the job satisfaction process. Structural equation modelling (SEM) is used here to analyze the data presented in CSR. The proposed model improves the overall feasibility and efficiency of employees.

Haerens et al. [ 23 ] introduced a job satisfaction method for school teachers. The main goal of the technique is to predict teachers’ chaotic leadership styles. The chaotic leadership style contains both positive and negative emotions among the teachers. The educational style and presentation are also detected based on teachers’ performances. The introduced method identifies the exact job satisfaction range of teachers.

Cheung et al. [ 24 ] designed a new job satisfaction prediction strategy for the indoor environment. The workers’ indoor environment quality (IEQ) ratio is predicted using analysis tools. IEQ parameters provide necessary information that minimizes the complexity of the job satisfaction detection process. The IEQ parameters also contain the workers’ workspace environment. The designed method improves the overall job satisfaction level of workers.

Samerei et al. [ 25 ] developed a classification and regression tree (CART) algorithm-based job satisfaction assessment process. The CART algorithm mainly extracts features presented by bus rapid transit (BRT) drivers. BRT is used here as input to produce relevant data for the assessment process. BRT drivers provide details such as their mental health state, stress ratio, behaviours, and body condition among drivers. The developed assessment method maximizes the efficiency and performance range of organization workers.

Karaferis et al. [ 26 ] proposed a job satisfaction method using factor analysis for healthcare systems. The factor analysis technique is used here to analyze the important factors necessary for detection. The main aim of the technique is to improve the quality of service (QoS) of employees. Experimental results show that the proposed method enhances healthcare systems’ significance and feasibility.

Seok et al. [ 27 ] developed an artificial neural network (ANN) based job satisfaction method for educational institutions. Multiple regression analysis is used to analyze the actual relationship between teachers and students. The regression analysis approach decreases the computation process’s time and energy consumption. The developed ANN method accurately predicts the technical factors that the teachers use.

Moslehpour et al. [ 28 ] introduced a configurational approach to job satisfaction (JS). The actual aim of the approach is to analyze the job satisfaction range of the workers. The emotional competence (EC) and positivity ratios of workers are identified. EC produces optimal data that minimizes the latency in the JS detection process. Compared with other approaches, the introduced approach increases the workers’ tendency and ethical ambiguity level.

Grolleau et al. [ 29 ] developed an empirical analysis method for job satisfaction in French firms. A lagged predictor is used in the method to predict the necessary variables for the detection process. The developed method also produces innovative ideas and techniques to improve the overall job satisfaction range of the employees. The developed analysis method improves the performance and efficiency range of employees.

Sung and Hu [ 30 ] proposed a new detection method using job satisfaction. The technique aims to identify the exact impact of airline internal branding based on work outcomes. The identical idea, working ability, communities, productivity, and leadership ratio of workers are detected in the database. The detected data provide optimal data for the job satisfaction detection process. The proposed method increases the feasibility and effectiveness organizations.

Chang et al. [ 31 ] designed a regression analysis-based job satisfaction detection method for healthcare centres. The developed method is mostly used in rural areas to identify the impacts of electricity and lights in healthcare. The designed method also predicts the issues that occur while providing emergency services to users. The developed method minimizes the complexity level of providing services to users in healthcare centres.

Quantitative assessments are fond of various impacting features/ attributes subject to fluctuations. Based on the various impacting features, a professional’s job satisfaction level and personal development are tedious to analyze. The study is due to internal and external demands and data circulation. By augmenting productivity-dependent data, the variations are less identified, regardless of the optimization function defined. This article introduces a fuzzy derivative-based assessment method to mitigate such issues in data handling.

Compared to traditional methods of measuring satisfaction on the job, the proposed method has far more beneficial aspects. For instance, compared with existing qualitative methods, the proposed model’s quantitative precision provides a more exact measurement, allowing for unambiguous and tangible findings about satisfaction levels. Then, the process guarantees that characteristics particular to each employee are considered by adapting assessments to their experiences and personal development. By incorporating fuzzy logic, the evaluation process can be more efficient and effective in optimizing work satisfaction levels while decreasing analytical overhead. The proposed technique facilitates continuous improvement by enabling continual adjustments in response to feedback and evolving conditions.

3 Data Representation

The “employee satisfaction index dataset” from [ 32 ] is used throughout the article to validate the best-affordable solution. Figure  1 presents the data representation with impact from the considered dataset.

Data representation

The attributes from the given dataset are split into personal, designation, and assessment metrics. From this data, the certifications, awards, and level attributes are the impacting features for the satisfaction determination as either yes or no (refer to Fig.  1 ).

4 Quantitative Assessment Method (QAM) for Improving Satisfaction Levels

The QAM- is designed to improve the job satisfaction of college students based on their experience and personal development inputs. The inputs from the college students are obtained, (i.e.) the partial derivative is observed each quarter. The main objective of this quantitative assessment method is to reduce errors in analyzing experience levels and personal development. The challenging task in this proposed method is the differential augmentation and correlation of two factors using fuzzy assessment for extracting the partial derivatives with the previous successive quarter validation. The partial derivatives are stored as records for the previously validated instances. The processes involved in the QAM-FL are represented in Fig.  2 .

Processes in QAM-FL

The experience and personal development of the college students are observed through their performance analysis for step-in to career progression. The observed inputs are classified as experience levels and personal growth. In experience analysis, the activity of college students in each quarter is said to be continuous and similar based on time and day. In contrast, this data is not observed on consecutive days in a personal development analysis, such as skill assessment/career awareness for the time/day. The partial derivatives are extracted using the min–max functions of the fuzzification process. After achieving the maximum factors, the remaining derivatives are halted by fuzzy logic. In this process, the proposed method optimized the validation based on cumulative experience and individual satisfaction levels shared by the students in different quarters. The best-afforded job satisfaction level is detected using this minimum and maximum derivative for various progression levels. The validation and identification process reduces the chance of analysis overhead and time spent utilizing the fuzzification process. The analysis overhead is detected using the available derivatives for different progression levels. The proposed QAM focuses on such analysis overhead through extracted partial derivatives using fuzzy logic. The Initial quantitative assessment of college students’ job satisfaction is validated using the sequence of experience and personal development \(\text{Exp}\left(Q\right)\) and \(\text{Pdv}\left(Q\right)\) identified/observed in different quarters, such that the job satisfaction \({\text{Job}}^{\text{Satif}}\left(Q\right)\) is expressed as

In Eq. ( 1 ), the variable \({C}^{\text{e}}\) represents the causing error at each quarter analysis, and the objective of reducing the causing error is for all \(Q\left(\text{Exp}*\text{Pdv}\right)\in {\text{Job}}^{\text{Satif}}\left(Q\right)\) is defined as validating successive quarters for job satisfaction. The quarter is divided into two factors (i.e.) experience \(({Q}_{\text{Exp}})\) and personal development \(({Q}_{\text{Pdv}})\) . This proposed method implies that in the job satisfaction analysis based on experience and personal development, the QA-based method will validate the best-afford job satisfaction level based on partial derivatives conditions, and the aid of fuzzy optimization is to identify all possible job satisfaction of the placed students \(\left(\Delta \right)\) is expressed as in Eq. ( 2 )

According to the above conditions, the partial derivatives of all students can be continuously validated.

Equation ( 3 ) computes the maximum possible job satisfaction of all the placed students for a prolonged time \(T\) . The above validation is pursued, augmenting job satisfaction and retaining students at \(T\) . If \(V\) means the optimal job satisfaction validation of different newly placed students. Table 1 presents the derivatives and \({C}^{\text{e}}\) they were observed during two-quarters of the four levels of the job presented in the data source.

Table 1 presents the \({C}^{\text{e}}\) and the derivatives used to validate the performance of the employees in 2 different quarters. This quarter varies for the various levels (job designation) for \(\text{pdv} (Q)\) and its corresponding \({C}^{\text{e}}\) . The appraisal and performance estimations are considered satisfied as the derivatives are fewer. Therefore the \({C}^{\text{e}}\) is less based on different dataset attributes and quarters. Hence, the successive quarter is identified with the condition \(Q={Q}_{\text{Exp}}+{Q}_{\text{Pdv}}\) such that the proposed assessment improves college students’ experience and personal development across different quarters. If \({\exists }^{d}\) means the partial derivative is validated from the model mock interviews, then \({\exists }^{d}=\left({\text{Std}}^{n}\times Q\right)-{C}^{e}\) the partial derivative observed instance will be classified using the min–max functions. The variable \(Fzy({\text{Q}}_{\text{Exp}})\) and \(Fzy({Q}_{\text{Pdv}})\) represent the fuzzy optimization of \({\text{Job}}^{\text{Satif}}\left(Q\right)\) is observed from all the students \({Std}^{n}\) in \(i\) quarters is observed such that

As per Eqs. ( 4 ) and ( 5 ), the partial derivatives are extracted using the maximum and minimum functions of the fuzzification process observed from the instances are mapped with \(\text{Exp}\left(Q\right)\) and \(\text{Pdv}\left(Q\right)\) . Based on the extracted partial derivatives, Eq. ( 1 ) is re-written as

For the expanded job satisfaction assessment, the sequence of \(\left({\text{Std}}^{n}\left(Q\right)\in T\right)\) is to validate job satisfaction for all placed students and retain them for a prolonged time. In this assessment, the first-year student experience and personal development analysis are shown in Eq. ( 6 ). The job satisfaction measure \({\text{JS}}^{m}\) is validated to identify analysis overhead based on partial derivatives using min–max functions. In this analysis, the above derivatives for the factors considered in Table  1 are used for computing \({J}^{\text{sat}}(Q)\) for different levels. This is presented in Table  2 .

In Table  2 , the \({J}^{\text{sat}}(Q)\) is validated based on the experience and fuzzy \(\left({Q}_{\text{Adv}}\right)\) from which min–max variations are identified. This computation is optimal for \({\exists }^{\text{d}}\) such that the conditions in Eq. ( 3 ) are validated. Therefore, the optimal fuzzification is performed using \({J}_{S}^{m}\) such that the Fuzzy \(\left({Q}_{\text{Pdv}}\right)\) is relied upon \(\Delta\) . From the \(\Delta\) the \({\exists }^{d}\) are extracted using \(\text{Exp}(Q)\) and \({C}^{\text{e}}\) from the previous inputs. In this case, the validations are performed using \(\text{min}\left|\sum \text{Exp} \left(Q\right)+\text{Pdv}\left(Q\right)\right|\) where \({C}^{\text{e}}\) stands for experience, which is never less than a lagging factor. Thus the \({\exists }^{\text{Sat}}(Q)\) is handled using different \({J}_{S}^{m}\) such that \(\sum_{\text{Std}=1}^{T}.\) satisfies \(\sum_{r=1}{V}_{\Delta }\) represents for fuzzy assessment. The correlating of experience and personal development of the placed students across different quarters is validated using the available data through consecutive processes. For this validation, the sequence of \({\text{Std}}^{n}\left(Q\right)\in T\) is expressed as

The above Eq. ( 7 ) follows the partial derivatives of all students, which can be continuously validated using the sequence of the previously stored data for achieving successive quarters. It is analyzed based on two factors that validate differentially. Therefore, based on the proposed method assessment, \({\text{Job}}^{\text{Satif}}\left(Q\right)=Fzy\left({Q}_{\text{Exp}}\right)+Fzy\left({Q}_{\text{Pdv}}\right)\left[1-T\left({\text{Std}}^{n}\left(Q\right)\right)\right]\) is the final output without causing errors and analysis overhead. The final job satisfaction measure based on minimum and maximum derivatives \(({\mu }_{\left({Q}_{\text{Exp}}\right)})\) and \(({\mu }_{\left({Q}_{\text{Pdv}}\right)})\) for experience and personal development validation at the first level is expressed as

Equations ( 8 ) and ( 9 ) validate that the job satisfaction of the placed students is observed using the experience and personal development for the sequence across different quarters through partial derivatives. In this initial assessment method, the validation of \({\mu }_{\left({Q}_{\text{Exp}}\right)}\) , \({\mu }_{\left({Q}_{\text{Pdv}}\right)}\) , \(Fzy\left({Q}_{\text{Exp}}\right)\) and \(Fzy\left({Q}_{\text{Pdv}}\right)\) represents the serving inputs for the successive quarter identification. The consecutive processing of fuzzy optimization helps to identify the analysis overhead in this proposed method. This fuzzification process is discussed in the following section. Where, \(\left(\frac{\text{Exp}\left(Q\right)+\text{Pdv}\left(Q\right)}{{C}^{\text{e}}}\right)\) is the job satisfaction level observed from the individual student based on partial derivatives extracted for different progression levels. Based on Eqs. ( 8 ) and ( 9 ), the variations across \({J}_{S}^{m}\) is validated in Fig.  3 .

Min–max variation for \({J}_{S}^{m}\) analysis

The satisfaction measure is computed using \(\text{Exp} (Q)\) and \(\text{Pdv}(Q)\) from the post. Based on the fuzzy optimization across various fuzzy \(\left({Q}_{\text{Pdv}}\right)\) and Fuzzy \(({Q}_{\text{Exp}})\) the variations are computed. Therefore, successive quarters are required to suppress the variations across multiple \({Q}_{\text{ex}}\) or \({Q}_{\text{Pdv}}\) such that \({J}_{S}^{m}\) based sequenced are optimized. This validation is normalized for \(\mu \forall {Q}_{m}\) . The optimality is retained using variation suppression. This variation is suppressed using different entries of \({\text{Job}}^{\text{sat}}(Q)\) provided \(T ({\text{std}}^{n}(Q)\) retains maximum \({J}_{S}^{m}\) (Refer to Fig.  3 ). At a prolonged time, QAM identifies the best-afford satisfaction level when validating the students’ appropriate experience and personal development for the next validation. This validation improves job satisfaction levels and retains students for different progression levels. According to the above condition, the two factors are differentially validated using fuzzy optimization between students’ jobs and satisfaction level, and min–max derivatives can be obtained, which is the final quantitative output of the placed student.

In Eq. ( 10 ), the college students’ experience and personal development must achieve the best-afford job satisfaction level and validate the partial derivatives using min–max functions. The quantitative assessment measure \({\text{QA}}_{m}\) represents the expected job satisfaction level to be met when extracting partial derivatives. After the maximum factors are satisfied using partial derivatives by fuzzy optimization, the job satisfaction level of the individual student will be validated, and analysis overhead will be identified in advance with progression levels. In this process, when the student’s job satisfaction level exceeds the threshold, the fuzzy process stops the derivatives. Otherwise, it will continue to identify best-fit features using min–max derivatives for extracting optimal output, and this process will continue for a prolonged time. In the fuzzification process of \({\text{QA}}_{m}\) and min–max derivatives, the proposed method is to reduce the analysis overhead and time as much as possible across different quarters. Therefore, the objective function \(F\) is computed as in Eq. ( 11 )

5 Fuzzy Optimization Based on Min–Max Derivatives

This article maximizes college students’ best-afforded job satisfaction and improves their experience and personal development to achieve optimal output using the available derivatives. The proposed method will optimize the extraction of partial derivatives defined in the previous section. This section introduces the implementation process of fuzzification to validate the students’ job satisfaction level. The goal is to identify the best-fit feature based on fuzzy optimization. In this sequential process, the QAM validates the individual student job satisfaction level, the output result differs for each student, and the analysis overhead is reduced. The output of the input student’s job satisfaction with min and max derivatives can be expressed as in Eq. ( 12 )

The proposed method extracted optimal output using individual satisfaction levels and cumulative experience shared by the students. In addition to the job satisfaction level and optimization output, a hidden layer handles multiple min and max derivatives for extracting optimal output across different quarters. The derivatives based on min–max variations and their applicability are verified using the factors mentioned in Table  3 .

The different levels of jobs are correlated with quarters based on their attributes such that \(\mu\) are balanced between the min and max values. The fuzzification is balanced for \(\mu \left({Q}_{\text{Pdv}}\right)\) and \(\mu \left({Q}_{\text{Exp}}\right)\) such that \({J}_{S}^{m}\) is observed for successive quarters. In this process, the \({Z}_{i}\) is optimized using \(F\) provided \({\text{QA}}_{m}\) is high, and therefore, the consecutive measure is validated as \(\left[\text{Exp}{\left(\text{Q}\right)}_{\text{T}}+\text{Pdv}{\left(Q\right)}_{T}\right]\) . These features are verified using Eq. ( 3 ) for the conditional fuzzy assessment. In this conditional assessment, the derivatives are summoned to detect the best-afford solution. Therefore \({\exists }^{d}\) is used for balancing \(F\) for \(f(Q)\) and \({A}_{\text{o}}\left(Q\right)\) . The partial derivatives are extracted using min–max functions of the fuzzy process. The student job satisfaction level and output of the hidden layer are expressed in Eqs. ( 13 ) and ( 14 )

where, \(f\left(.\right)\) represents the minimum and maximum functions of the fuzzification process. This corrects the extraction of optimal output and enhances satisfaction levels. Therefore, the experience analysis of the student can be expressed as in Eq. ( 15 )

The fuzzy optimization uses the extracted partial derivatives to continuously satisfy the satisfaction levels and identify the analysis overhead toward the minimum. The analysis overhead function is expressed as in Eq. ( 16 )

The conditions for achieving the best-afford job satisfaction level are expressed in Eq. ( 17 )

In this process, the output of the proposed method is compared with other existing methods to identify the best-afforded job satisfaction level for each student. The above condition determines the occurrence of analysis overhead in different quarters and the occurrence of min–max function observation. In this, extracting optimal output achieves \({H}_{1}=0\) as the previous output based on experience and personal development analysis such that \(\in\) maximum factor, where the improvement is needed. Therefore, this is not considered in the fuzzy process. This best-afford solution extraction based on variation optimization is analyzed in Fig.  4 . This analysis confirms the fuzzy assessment output considering \(F\) .

Best-afford solution based on variation analysis

The best-afford solutions using \(f\left(Q\right)\) for \(\mu\) and \({\text{QA}}_{m}\) are validated in Fig.  4 . Considering the derivatives \(\forall {Q}_{\text{Exp}}\) and \({Q}_{\text{Pdv}}\) , the \(F\) is satisfied by variation suppression. This process optimizes the flow of \({A}_{\text{o}}(Q)\) for multiple derivatives providing new suggestions on best-afford satisfaction outputs. In the \({J}_{S}^{m}\) Validate that the outputs vary across the min–max (variation) suppression. If this case is satisfied, then the maximum best-affordable solutions are achieved (refer to Fig.  4 ). The best-fit feature is observed at the end of all the fuzzy assessments or before the start of the next evaluation. Identification for different progression levels and minimum and maximum derivatives are observed at the best-afford job satisfaction level. Therefore, the best-fit feature holds the minimum and maximum derivatives where the first job satisfaction level is validated and later causing errors are minimized. It is to be analyzed that student experience and personal development are based on job satisfaction level validation for extracting optimal output. This observation of partial derivatives is valid from the min–max derivatives until maximum factors are achieved. Instead, the occurrence of \({A}_{\text{O}}\) continuously changes both minimum and maximum functions until the fuzzification process halts it. The identification of causing error due to less experience and less personal development observed from the students using conventional methods. Now, the job satisfaction validation (i.e.) \({\text{ Job}}^{\text{Satif}}{\left(Q\right)}_{T}\) with analysis overhead and time in experience and satisfaction level, analysis is the final observed sequence of \({\text{QA}}_{m}\) . If \(\text{Exp}\) and \(\text{Pdv}\) are not analyzed, then the whole class of \({\text{Job}}^{\text{Satif}}{\left(Q\right)}_{T}\) will be processed under min–max derivatives, resulting in high satisfaction analysis and experience extraction.

6 Discussion

This section presents a briefing on the impact of assessment on experience extraction, satisfaction analysis, quantitative output, analysis time, and overhead. Regardless of the method-based comparisons, the job satisfaction measure varies by 0.4, 0.6, 0.8, and 1 for analyzing the impact of the metrics. In this analysis, the number of students considered is 120 (the maximum), and the experience for a maximum of 48 months is accounted for.

Figure  5 represents the partial derivative extraction for student job satisfaction levels based on experience and personal development analysis. In the proposed method, the validation of the two factors is maximized by identifying accurate satisfaction levels using the fuzzification process. The initial quantitative assessment of validating the job satisfaction level of different newly placed students in college was based on experience analysis in various quarters to identify the best-afford satisfaction level. For this process, the two factors are differentially validated through available derivatives, whereas experience extraction identifies some errors due to min–max derivatives at previous levels. Therefore, the QAM is modelled to determine the best-afford job satisfaction levels in any quarter; the experience and personal development are better findings. Based on the occurrence of min–max functions of the fuzzification process for the instances, \({\text{Exp}}_{T}\) and \({\text{Pdv}}_{T}\) belongs to \(\text{arg}\underset{Q}{\text{min}}\sum {C}^{\text{e}}\left(Q\right)\forall Q\left(\text{Exp}*\text{Pdv}\right)\) it identifies the causing errors and analysis overhead. Therefore, the number of experience extraction leveraging the job satisfaction validation is high in the proposed method in a continuous manner. Detecting such derivatives improves job satisfaction such that \(Q\left(\text{Exp}*\text{Pdv}\right)\in {\text{Job}}^{\text{Satif}}\left(Q\right)\) represents the highest experience extraction in this article.

Discussion on experience extraction

In Fig.  6 , the experience and personal development of the students across different quarters are analyzed to improve job satisfaction and retain students for a prolonged time based on validation and partial derivatives for extracting optimal output. The best-fit features are extracted from the different quarters mapping in time intervals. The job satisfaction level is validated for other students based on their experience analysis to identify the best-afford job satisfaction level. The fuzzy assessment gives outputs in continuous satisfaction level identification with partial derivatives and min–max functions for time. The partial derivatives are extracted based on the available derivatives and the experience associated with job satisfaction. This is performed using the min–max functions to satisfy both the condition \(Fzy({Q}_{\text{Exp}})\) and \(Fzy({Q}_{\text{Pdv}})\) to identify the optimal output and error occurrence. The analysis overhead and time are determined using fuzzy assessment and achieved in successive quarters, preventing overhead. The error occurrence is minimized for achieving high satisfaction analysis using the proposed method for both conditions satisfying high quantitative output using min–max derivatives.

Discussion on satisfaction analysis

The optimal job satisfaction validation of different newly placed students based on partial derivative extraction is observed for identifying the best-afford job satisfaction level is depicted in Fig.  7 . In this proposed method satisfies less analysis overhead and error occurrence by mapping the condition \(\text{Exp}\left(Q\right)\) and \(\text{Pdv}\left(Q\right)\) using fuzzy logic. In this, the partial derivatives of all students can be continuously validated by analyzing individual students’ experience and personal development in different quarters. The \(\text{Exp}\left(Q\right)\) and \(\text{Pdv}\left(Q\right)\) is validated until the maximum factor is identified from the available derivatives. The fuzzification process requires minimum and maximum functions based on the extracted partial derivative, reducing analysis overhead at the time of job satisfaction level identification for the students. The error is identified using the min–max derivatives to compute the job satisfaction measure based on the available correlation between the experience and personal development of the placed students across different quarters. It is validated using the available data through consecutive processes. Therefore, the optimal derivatives are extracted for experience and personal development analysis. Hence, this proposed method identifies the satisfaction level using min–max functions computed across multiple derivatives, and the quantitative output is high.

Discussion on quantitative output

The analysis overhead and time are identified at the fuzzy assessment across different quarters for students’ job satisfaction level detection through partial feature extraction, which is illustrated in Fig.  8 . This proposed method satisfies the minimum analysis time by computing the satisfaction level and cumulative experience of the individual student based on the improvement in personal development, and its post-maximum factor is accurately identified for retaining students for a prolonged period of time. In this manuscript, the condition \({\text{Job}}^{\text{Satif}}\left(Q\right)=Fzy\left({\text{Q}}_{\text{Exp}}\right)+Fzy\left({Q}_{\text{Pdv}}\right)\left[1-T\left({\text{Std}}^{n}\left(Q\right)\right)\right]\) is validated for achieving optimal output. The causing errors and analysis overhead is mitigated due to min–max functions until best-fit feature identification wherein the different progression levels are preceded using Eqs. ( 4 ), (5), (6), (7), (8), and (9) computations. In this proposed method, experience and personal development are analyzed to identify the satisfaction level. Based on this validation, the analysis time is less than the other factors in this article.

Discussion on analysis time

In Fig.  9 , the analysis overhead identified in the proposed method is considerably less due to different satisfaction levels, and min–max derivatives can be obtained for individual students observed from various quarters. Based on the consecutive validation of fuzzy assessment, it helps to improve job satisfaction levels and identify the analysis overhead using partial derivatives. The condition \(\left(\frac{\text{Exp}\left(Q\right)+\text{Pdv}\left(Q\right)}{{C}^{\text{e}}}\right)\) is used to achieve optimal job satisfaction level observation for the individual student depending on extracted partial derivatives for different progression levels. This validation uses the fuzzy assessment and min–max functions for best-afford job satisfaction level detection, which helps map the two factors across different quarters. After this fuzzification process, the experience and personal development analysis based on job satisfaction level is identified for the individual student to reduce analysis time and overhead. This fuzzy assessment and quantitative output help to reduce the maximum factor with the validation, improve job satisfaction levels, and retain students for different progression levels in other quarters, reducing analysis overhead in this proposed method.

Discussion on analysis overhead

7 Conclusion

The student’s job satisfaction level is assessed with the help of a quantitative assessment method with fuzzy logic. The assessment is performed according to workplace attributes and personal development. A professional’s experience is initially computed to identify their personal growth and willingness. The fuzzy optimization is applied to derive the partial derivatives for every attribute and identify the maximum best-afford solution. The minimum and maximum values for cumulative experience and variations are computed considering individual satisfaction levels. The computations are validated using different fuzzy derivatives and their impact on the partial derivatives across multiple considerations. The best-afford solution condition is validated using min–max progression and its optimal output. Therefore, this proposed method handles less complex quantitative outputs for different experienced persons over differential satisfaction levels. The proposed quantitative assessment method with fuzzy logic shows promise for evaluating job satisfaction among student professionals. However, its reliance on subjective inputs and its effectiveness across diverse considerations are still limitations. Thus, future work will focus on resolving these constraints on the proposed model by developing a more objective fuzzy model that can handle multi-attribute experience and personal data development. With this improvement, the future scope aims to improve the precision and reliability of quantitative measures.

Availability of Data and Material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Cheng, Y. Fuzzy Logic-Based Quantitative Development Model for Job Satisfaction in College Graduates. Int J Comput Intell Syst 17 , 226 (2024). https://doi.org/10.1007/s44196-024-00637-y

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Received : 29 November 2023

Accepted : 13 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s44196-024-00637-y

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