Becoming a Hedge Fund Quant: My OMSA Journey

Background

I learned so much in OMSCS (https://etlq.github.io/omscs/), and decided to continue my journey with OMSA. At this point in my career, the marginal benefit of adding another Georgia Tech degree on my resume is close to zero. So my motivation is purely for the sake of lifetime learning.

edX MicroMasters (MM) program

Because I applied to OMSA after I finished OMSCS, there was a 'gap' semester until I could start OMSA. Luckily, you can enroll into OMSA core courses (isye6501, mgt6203, cse6040) through edX MM program. Credits can be transferred once you officially become an OMSA student later. So I decided to take ISYE 6501 on edX platform.

ISYE 6501: Introduction to Analytics Modeling (iAM)

It's a well designed course. This should be the first course for every OMSA student. The lecture style is the polar opposite to ML/RL courses at OMSCS. Prof Sokol did an amazing job of condensing so much content into a series of really concise lecture videos. The majority of the course grade comes from three proctored exams which were full of tricky wording but they were manageable.

How to transfer credit from edX to OMSA

The process was straightforward. Once I started OMSA, the admin office sent an email with a form which takes only a few minutes to fill. Mostly they just need the course name and edX id, and they can do the rest. I got credit transfer approved a few weeks later. It's a bit sad because I studied hard for the exam in ISYE 6501 to score 90+% to secure the letter grade A, but the transferred credit doesn't count toward GPA after all (which I knew beforehand, but still).

CSE 6040: Computing for Data Analytics

I've opted out of this course, based on my prior experience in Python/Numpy/Pandas.

MGT 6201: Business Fundamentals for Analytics

I've also opted out of this course, since I took similar courses previously.

OMSA basic core courses: opt-out process

The opt-out process was straight forward. A few weeks into the semester, they sent out the form via email, which took 3 minutes to fill. A few weeks later, they emailed approval notification. The optout request form was so simple, as in "rate your familiarity with the subject on a scale of one to ten" and I just selected ten. They never bothered to verify my claim. They took it as is. So this means anyone can opt out of any basic core courses in OMSA. I guess they don't care because why would they care if a student wants to replace easy intro courses with harder advanced courses.

MGT 6203: Data Analytics in Business (DAB)

I don't have a lot to say about this course. It's too rudimentary. They really should make this course optional.

During the semester, my manager left the company. The new manager was toxic and many team mates left. So I started job hunting. I wish I didn't have to, but it happens. As the old cliche says: employees don't leave the company. They leave their boss. A few months later, I received a new job offer to join a quant investment team at a hedge fund as a quant researcher.

For those of you interested in quant finance, I recommend at least try once working in a pod team. "Pod" means an investment team with its own allocated capital. Usually the team size is only 2 ~ 5 people. And there are many pod teams (e.g. 100+ such teams in a large hedge fund), each building its own trading strategies.

The team gets paid X% of profit it makes (where the actual value of X is determined based on the contract your team negotiated in advance). If the team makes no money, then your bonus is zero, and likely the whole team gets fired. If the team makes a lot of money, then your bonus can be millions of dollars. This is "alpha" quant, and the stake is as high as it can get.

There are other quant roles in hedge funds & investment banks, but often they are 'risk' quants, or 'execution' quants, or 'pricing' quants. Their job is more like maintaining existing models, assisting traders, and generating analytics for product/sales teams. They are important but more like regular jobs with stability.

Non-Compete Period

I handed in resignation to my employer to take this new hedge fund job. My employer enforced a few months of non-compete: it's a common practice in the financial industry where previous employer keeps paying you to not work for competitors for pre-determined duration. Now I suddenly had a few months of forced vacation until my next job starts. So I decided to take two courses in the next semester: DL and FM.

CS 7643: Deep Learning (DL)

I wanted to take this class while in OMSCS, but didn't get to. So here I am. The lecture quality is mediocre but the assignments are fantastic. They get you hands-on with implementations of DL from scratch, followed by a lot of coding PyTorch (which is the industry standard DL library developed by Meta) to implement CNN, LSTM and Transformer models. The assignments also have a section for reading & summarizing recent DL papers, which is educational & rewarding. The course is overall difficult and time consuming. I spent 20 hours per week.

MGT 8813: Financial Modeling (FM)

It's ok as an introductory course. They give you a tour of corporate financial statements: balance sheet, income statements and cashflow statements. The lecture spent way too much time on Excel exercise. This course alone will not give you anything directly useful to a career in finance, but gave me enough to build on with my own further study.

ISYE 6644: Simulation (SIM)

This was a fun pencil & paper math/stats course. I was worried because it had been 1000 years since I took a proper math course previously. But it turned out both workload and difficulty were manageable. Prof Goldsman's lecture is so well presented. His sense of humor makes otherwise dry math material engaging. In terms of practicality and applicability to my day job in quant finance, it was not super directly useful.

ISYE 6414: Regression Analysis (REG)

It's a good course to learn all the fundamentals of regression modeling. Its focus is more on application than theory. The lecture gives a plenty of real world case studies of regression analysis on topics like SAT scores and Covid infections, which I found informative. The coding assignments are not difficult but massive & time consuming. The exam has a closed-internet, timed, proctored coding section. So if you encounter error/bug that you cannot fix on the fly, then it can cripple your grade.

ISYE 6669: Deterministic Optimization (DO)

It's turned out to be the best course I've taken in the program. At the core of every ML algorithm, it's solving some form of optimization problem. This course is all about how to formulate and solve various optimization problems. The lecture quality is great. Weekly homework is a well designed exercise to get hands-on with the materials. The lecture also shows real world examples, such as how power companies decide how much electricity to generate at each plant, which was nice. This course really prepares students for any advanced ML courses (such as ISYE 6740 & CS 7643)

ISYE 6740: Computational Data Analysis (CDA)

Come back here in late May 2026 for my review !