Carnegie Mellon University

Careers FAQ

This is a niche degree and it is important to the Admissions folks that you know enough about the industry to be confident that a career in quantitative finance is what you want to do. However, many enter this program with only a few undergraduate summer internships behind them. Part of the value in being a part of the MSCF program is the opportunity to learn about the quantitative finance career paths through fellow students with industry experience, practitioners in the classroom, the MSCF Speaker Series, various alumni engagements and, of course, the summer internship.

Yes, a handful of our graduates have gone on to earn PhDs. However, the MSCF program is designed to prepare graduates for a job in the industry immediately upon graduation. If your interest is in academia, you should apply directly to a PhD program.

While certain roles in model validation and fixed income research commonly require a PhD, the opportunities for well-equipped masters students are many, including quantitative research. Indeed, the very reason this program was begun in 1994 was to bridge the gap that existed at the time between the PhD’s possessing the needed quantitative skills but lacking the commercial understanding of its application and the MBA with strong communication skills and and solid market knowledge but without the math, statistics and coding needed by the banks and hedge funds. Strong quant skills coupled with market knowledge and the ability to communicate is what the MSCF degree offers the employer and it has been a winning combination. 

The MSCF degree will better prepare you for a position as a data scientist in the finance industry, when compared with either a MS in Data Science which covers a broad range of applications or an MS in Business Analytics which focus is on improving a firm's performance using data-driven decision making. Neither of these programs has the laser focus of MSCF, applying machine learning and other data science tools to quantitative finance. Taught with mathematical rigor coupled with the necessary computational skills provided by our computer science courses, students emerge from our program exceptionally well prepared to adapt and assimilate new methods of data analysis that appear in this rapidly-expanding field.