MS in Computational Finance

2 Years On Campus Masters Program

Carnegie Mellon University

Program Overview

The Master of Science in Computational Finance (MSCF) at Carnegie Mellon University is a prestigious interdisciplinary program designed for students who want to master the quantitative foundations of modern finance. Combining mathematics, statistics, computer science, machine learning, and financial engineering, the program prepares graduates for highly successful careers in quantitative finance, risk management, trading, asset management, and financial technology.

Curriculum Structure

Pre-Program Preparation

Before beginning formal coursework, students complete the MSCF Summer Launch Program and intensive orientation. Through MSCF Math Prep, MSCF Probability Prep, MSCF Programming Prep, and MSCF Markets Prep, students strengthen their foundations in mathematics, probability, Python programming, financial markets, and professional communication, ensuring they are prepared for the analytical demands of the program.

Year 1

Students build a rigorous foundation in quantitative finance through coursework focused on stochastic modeling, financial engineering, programming, and analytics. Students master topics such as Stochastic Calculus, Financial Data Science, and Risk Measurement Techniques, while developing expertise in Python programming, regression analysis, time series analysis, and quantitative modeling.

The curriculum also emphasizes teamwork and professional development. Through collaborative financial engineering projects and communication workshops, students learn to present complex quantitative concepts, develop practical solutions, and work effectively in interdisciplinary teams.

Final Semester

In their final semester, students customize the program to align with their career ambitions through advanced electives and specialized quantitative finance coursework. Students deepen their expertise in areas such as derivatives, algorithmic trading, risk analytics, machine learning in finance, portfolio management, and financial technology while completing industry-focused projects that mirror real-world financial challenges.

Focus Areas

Computational Finance; Financial Engineering; Quantitative Finance; Risk Management; Financial Data Science; Stochastic Modeling; Machine Learning in Finance; Portfolio Management; Derivatives Analytics; Algorithmic Trading.

Learning Outcomes

Develop expertise in financial modeling, stochastic calculus, financial data science, risk analytics, quantitative programming, machine learning applications, statistical analysis, portfolio optimization, financial engineering, and advanced problem-solving for complex financial markets.

Professional Alignment (Accreditation)

The program integrates finance, mathematics, statistics, and computer science into a highly applied curriculum designed for quantitative finance careers. Students receive extensive career preparation, internship support, industry exposure, and professional communication training, ensuring they are well prepared for leadership roles in financial institutions, hedge funds, asset management firms, and fintech companies.

Reputation (Employability Rankings)

The MSCF program is recognized as one of the world's leading quantitative finance programs and has been preparing students for careers in computational finance for more than three decades. The program benefits from an interdisciplinary faculty drawn from multiple Carnegie Mellon colleges, a global alumni network of more than 2,300 graduates, dedicated career services, and exceptionally strong employment outcomes, with the vast majority of graduates securing positions shortly after graduation.

Experiential Learning (Research, Projects, Internships etc.)

At Carnegie Mellon University, the MS in Computational Finance program is designed to provide students with a robust blend of theoretical knowledge and practical skills essential for success in the finance industry. Students engage in hands-on learning experiences that prepare them for real-world challenges. The program leverages state-of-the-art facilities and cutting-edge tools, ensuring that graduates are well-equipped to tackle complex financial problems.

Here are some key aspects of experiential learning within the program:

- Collaborative Projects: Students work on team-based projects that simulate real-world financial scenarios, fostering collaboration and problem-solving skills.
- Internship Opportunities: The program encourages internships with leading financial institutions, allowing students to gain valuable industry experience and network with professionals.
- Access to Advanced Software: Students utilize industry-standard software such as MATLAB, R, Python, and various financial modeling tools to analyze data and develop quantitative strategies.
- Research Laboratories: The program provides access to specialized labs, including the Financial Computing Lab, where students can conduct research and develop innovative financial solutions.
- Field Trips: Students have opportunities to visit financial firms and attend industry conferences, providing insights into current trends and practices in finance.
- Dedicated Libraries and Resources: The university's libraries offer extensive resources, including financial databases and journals, to support research and learning.
- Institutes and Centers: Students can engage with the Quantitative Finance Program and the Tepper School of Business, which host seminars and workshops featuring industry experts.

Progression & Future Opportunities

The MS in Computational Finance at Carnegie Mellon University equips graduates with the skills and knowledge to excel in high-demand roles within the finance and technology sectors. Alumni often find themselves in positions such as Quantitative Analyst, Risk Manager, Financial Engineer, and Data Scientist. The program's rigorous curriculum and strong industry connections ensure that graduates are well-prepared for the competitive job market:

- University Services: The Career and Professional Development Center offers tailored career coaching, resume workshops, and networking events specifically designed for Computational Finance students. Additionally, the university hosts career fairs that attract top employers in finance and technology.

- Employment Stats and Salary Figures: According to recent data, over 90% of graduates secure employment within six months of graduation, with starting salaries averaging around $100,000, reflecting the program's strong reputation and the high demand for skilled professionals in this field.

- University–Industry Partnerships: Carnegie Mellon has established partnerships with leading firms such as Goldman Sachs, JPMorgan Chase, and Citadel, providing students with internship opportunities and real-world projects that enhance their learning experience.

- Long-term Accreditation Value: The program is accredited by the Association to Advance Collegiate Schools of Business (AACSB), ensuring that the education you receive meets the highest standards of quality and relevance in the business world.

- Graduation Outcomes: Graduates of the program have gone on to work at prestigious firms and have reported significant career advancement, with many moving into leadership roles within just a few years of graduation.

Further Academic Progression: After completing the MS in Computational Finance, students have the opportunity to pursue further studies, such as a Ph.D. in Finance or a related field, which can lead to academic positions or advanced research roles. Additionally, graduates may choose to specialize further by obtaining certifications like the Chartered Financial Analyst (CFA) or Financial Risk Manager (FRM), enhancing their expertise and career prospects in the finance industry.