*In this article, Ellen Xiaoyue Sun describes how her studies in mathematics provide the perfect support for her career as a quantitative research analyst at JP Morgan Asset Management. Read more about her experiences and see whether a career in finance may be of interest to you.*

I graduated with a double major in math and psychology from New York University in 2017. After graduation, I joined Columbia’s Financial Engineering program for a Master degree, which is a popular choice for students interested in quantitative finance. Fellow graduates in my class pursued different careers such as traders, risk managers, quantitative strategists and more. I ended up in the investment industry and am currently a Quantitative Research Analyst at JP Morgan Asset Management. The goal of my team is to find stock selection indicators for our fund to be able to outperform the market in a consistent manner. The research work that my team does can be largely divided into two parts – working with large datasets to look for profitable stock selection signals and optimizing portfolio construction processes to maximize portfolio return while managing portfolio risk.

Now the question is: How does having a mathematical background help me to do my job?. If I have to pick one math class that is most important to my daily job function, statistics will have to be the winner. For example, we rely on various hypothesis testing methods to determine if a signal we find is actually meaningful to our process. Another big trend that has been going on in the investment community is machine learning. Machine learning is a set of algorithms designed for the computer to learn automatically through data to make future predictions. From the simplest models such as linear regressions to more complicated algorithms such as neural networks or ensemble approaches, statistical concepts are undoubtedly the building blocks of machine learning theories. On the other hand, similar to many other jobs, my job requires multidisciplinary knowledge in fields such as math, finance, economics, and coding. Besides specific knowledge such as statistics that I need for the job, mathematical intuition in general also serves as a good foundation to learn subjects mentioned above. For example, discrete math many times turns out to be important for computer science and calculus is the foundation of many important economic theories.

Although now it looks like I have found a starting point for my career, at one point or another during college, I certainly asked myself the exact same question again: Where will this degree lead me? After all, few recruiters care about how many hours I spent on abstract algebra or analysis. Some of the subjects we learn at school are application-based, i.e. directly applicable to a job functionality, such as accounting. But others are theory-based, such as math. Personally, I believe that many theory-based majors tend to provide a good knowledge base that is flexible and easily transferable to other disciplines, which was one of the main reasons why I chose math as major.

In a data driven world like today, new technologies are popping up at an unprecedented rate. I cannot possibly expect that what I learned in school will sustain me during my lifetime. However, what I do hope is what I learned in school will serve as a good foundation for my ongoing endeavor towards a meaningful career. To me, having a sound mathematical background is certainly a good way to build forth on a sound foundation.

### Ellen Xiaoyue Sun

New York University, Class of 2017

Quantitative Analyst, JPMorgan Asset Management,

New York, NY