Advice for breaking into Quant Finance while pursuing MS Data Science?

[VigQuest]

Hey everyone! I am new here. It's great to meet you all. I was hoping to get some insights about what steps I can take to break into Quantitative Finance as an MS Data Science student. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. I am seeking entry level roles. Below are some details about my background.

Current program: MS Data Science at Vanderbilt University
Current GPA: 3.925

Undergraduate program: BS Chemical Engineering with a Minor in Economics, at UC San Diego
Cumulative undergraduate GPA: 3.75

Math skills: Linear Algebra, Statistics, Probability, Calculus, Differential Equations
Computer skills: Python, R, SQL, MATLAB, Excel, PowerBI, Machine Learning, Deep Learning
Finance-related skills: Basic Economics and Econometrics (I focused on econometrics for my minor)

Work Experience:
Worked as a process engineer intern at an Oil & Gas company for 3 months. Mainly just simulated industrial processes using Aspen Plus software and analyzed the simulation data using Python.

What I am currently working on:
1. Learning the basics of python for financial trading (taking a DataCamp course, since I already paid for it through my fees):

• Using packages such as bt and TA-Lib
• Learning about indicators such as SMA, EMA, ADX, RSI, Bollinger Bands
• Learning about different kinds of trade strategies (trend-following vs. mean reversion)
• Evaluating strategy performance through backtesting and visualizing the results
• Learning about key strategy performance metrics, such as:
  • Rate of Return
  • CAGR
  • Sharpe ratio
  • Sortino ratio

2. Reading books, starting with Stochastic Calculus for Finance, by Steven E. Shreve

From my background, I think most people would agree that my biggest weak point is that I have had no formal financial training/education. The closest thing I have is taking some university coursework for economics and econometrics. Therefore, I was hoping to get some advice about what I could do to really beef up my finance knowledge while I'm still in school. I'm hoping that my MS coursework will cover the math and programming needed for entry level Quant roles, though I'm not sure if this is really the case (we don't cover stochastic calculus, for example, which is why I am trying to study it on my own).

After joining this platform, I also noticed that C++ appears to be quite prominent for Quant. Although it would obviously only help, I was just wondering if C++ is a must for entry level Quant roles, or could I land an entry level role with the languages that I do know, which I listed above?

That's all from me for now. If some experienced Quants on this platform could offer me some pearls of wisdom, I would greatly appreciate it. Is it feasible for me to break into Quant given my present foundation, or will I need to do an MFE (or some type of certification) first? Looking forward to being a part of this community!

 

[Quasar Chunawala]

Specifically on the finance bit - for sell-side QR roles, this can pretty much be picked up within the first 3-6 months on the job.

If you absolutely want to get familiarity with some products, I suggest 1) Financial Engineering and computation by Lyuu 2) Natenburg's options book, for good 'ol intuition.

Many people read John Hull's book(Options, Futures and other derivatives), but not a huge fan of it, as it feels like a cookbook, that is targeted towards MBA idiots.

 

[VigQuest]

Specifically on the finance bit - for sell-side QR roles, this can pretty much be picked up within the first 3-6 months on the job.

If you absolutely want to get familiarity with some products, I suggest 1) Financial Engineering and computation by Lyuu 2) Natenburg's options book, for good 'ol intuition.

Many people read John Hull's book(Options, Futures and other derivatives), but not a huge fan of it, as it feels like a cookbook, that is targeted towards MBA idiots.

Thank you for your suggestions! I'll look into the books that you mentioned.

 

[jarryds]

Self study and apply for full time roles. I always advise against a second masters. There is only one way to figure out if you can get into quant right?

Preference of programming language varies at the employer level. You will see which language is required in the job posting. Plenty of quant jobs like python and VBA (for sell side). I imagine most of the C++ roles are looking to take advantage of the benefits of using C++, which is probably outside of your realm of interest.

I know people like to suggest that quants don't need to know having an understanding of finance, but I would advise the opposite. Understanding finance and its participants is a really good way to determine what area of finance you want to operate in. Especially since you are going in blind - I would spend some time connecting with people in the industry (quant and non quant) and figuring out what you are interested in. This step is commonly skipped by people looking to get into the most math/cs rigorous positions, but I am getting the impression that isn't your approach.

 

[VigQuest]

Self study and apply for full time roles. I always advise against a second masters. There is only one way to figure out if you can get into quant right?

Preference of programming language varies at the employer level. You will see which language is required in the job posting. Plenty of quant jobs like python and VBA (for sell side). I imagine most of the C++ roles are looking to take advantage of the benefits of using C++, which is probably outside of your realm of interest.

I know people like to suggest that quants don't need to know having an understanding of finance, but I would advise the opposite. Understanding finance and its participants is a really good way to determine what area of finance you want to operate in. Especially since you are going in blind - I would spend some time connecting with people in the industry (quant and non quant) and figuring out what you are interested in. This step is commonly skipped by people looking to get into the most math/cs rigorous positions, but I am getting the impression that isn't your approach.

Thank you for your insights. I'm actually only looking at summer internships right now, because my MS is a two-year program. So I can't apply to full-time roles, since I have to return to my studies in the Fall.

Could you perhaps elaborate a little more on why you strongly advise against a second masters? That's always been somewhat on the cards for me. Is it simply because you don't think it's worth the cost, or is there something else?

I am definitely interested in increasing my understanding of finance and learning how to read the markets. In terms of networking, I am actually quite lucky since my father has been working in finance for 20+ years (non-quant). He is mentoring me and also connecting me with other finance people in his network. You are correct in your impression that my current aim isn't the hardcore math/CS positions (for example, Quant Dev). I'm trying to get more into the finance + analytics/modeling side of things. Although I have a question, is building financial computer models considered a hardcore CS role? I know people who have gone into financial modeling without CS degrees, but I'm wondering if these are the exceptions and not the norm (would MS Data Science be sufficient? Modeling and Machine Learning is part of our curriculum).

 

[jarryds]

Oh well then this is simpler - go for a quant (or quant adjacent) internship. That will give you a pretty good idea if you can make it in the field.

A second masters doesn't make any sense to me. At that rate, just go for a PhD. Most masters are terminal degrees (especially MFEs), so the entire point is to get a job after finishing it. If you want to spend 3-4 years in graduate school, you should be getting a good rate of return (a PhD). PhD's are also subsidized, so instead of burying yourself in loans, you are getting paid (although not much) to do it.

If you were coming from a non-stem area and already had a masters, I could understand the need to bridge that gap. But data science, while maybe not a rigorous as you may need, is STEM and should be able to put you in a position to bridge the gap. Quants get super caught up in education (which is very important), but I think we sometimes lose sight of the end goal. If your end goal is to be employed in quant, apply for quant positions. Self study, land a good internship, and take your resume to market. IMO an extra masters isn't going to make or break your application, but could dig you another 40-110k in loans that will grow at a crazy rate.

For your question, it really depends on the model and who you are working for. Banks aren't super tech savvy - a lot of their desks are still working off of excel sheets (and this will probably never change). It heavily varies for buy-side: some have pre-existing infrastructure that you build off of, some you need to develop by yourself (or with a team). It will depend on the size of the fund (small quantitative team -> more work, less hands). You need to be proficient in programming because that will be part of your job - this you can achieve without a degree in CS. Anything with analytics is python, VBA, SQL, and maybe R. There are very heavy CS driven roles in quant (just like there are heavy math roles), but that doesn't account for all of the available opportunities. If that isn't your interest or strength, look for different positions.

 

[Quasar Chunawala]

In your case, I strongly feel you can self-study & easily pivot to the desired role, without a second masters. You are a pursuing a masters in a STEM field from a good uni, which gives you a foot in the door. Beyond that, hiring managers don't care too much if specific skills are self-taught or school taught.

 

[VigQuest]

Oh well then this is simpler - go for a quant (or quant adjacent) internship. That will give you a pretty good idea if you can make it in the field.

A second masters doesn't make any sense to me. At that rate, just go for a PhD. Most masters are terminal degrees (especially MFEs), so the entire point is to get a job after finishing it. If you want to spend 3-4 years in graduate school, you should be getting a good rate of return (a PhD). PhD's are also subsidized, so instead of burying yourself in loans, you are getting paid (although not much) to do it.

If you were coming from a non-stem area and already had a masters, I could understand the need to bridge that gap. But data science, while maybe not a rigorous as you may need, is STEM and should be able to put you in a position to bridge the gap. Quants get super caught up in education (which is very important), but I think we sometimes lose sight of the end goal. If your end goal is to be employed in quant, apply for quant positions. Self study, land a good internship, and take your resume to market. IMO an extra masters isn't going to make or break your application, but could dig you another 40-110k in loans that will grow at a crazy rate.

For your question, it really depends on the model and who you are working for. Banks aren't super tech savvy - a lot of their desks are still working off of excel sheets (and this will probably never change). It heavily varies for buy-side: some have pre-existing infrastructure that you build off of, some you need to develop by yourself (or with a team). It will depend on the size of the fund (small quantitative team -> more work, less hands). You need to be proficient in programming because that will be part of your job - this you can achieve without a degree in CS. Anything with analytics is python, VBA, SQL, and maybe R. There are very heavy CS driven roles in quant (just like there are heavy math roles), but that doesn't account for all of the available opportunities. If that isn't your interest or strength, look for different positions.

Thank you for such a detailed and well-articulated response! I will definitely take your suggestions into consideration.

 

[VigQuest]

In your case, I strongly feel you can self-study & easily pivot to the desired role, without a second masters. You are a pursuing a masters in a STEM field from a good uni, which gives you a foot in the door. Beyond that, hiring managers don't care too much if specific skills are self-taught or school taught.

Thank you for the encouragement! I will keep self-studying to improve my skills.

 

[VigQuest]

Speaking of self-study, what subjects should I focus on? What are the must-have fundamentals for Quant? I already have some idea, but I would love to hear your takes on this (perhaps there is a topic that is not so obvious, but actually important, for instance).

 

[jarryds]

Speaking of self-study, what subjects should I focus on? What are the must-have fundamentals for Quant? I already have some idea, but I would love to hear your takes on this (perhaps there is a topic that is not so obvious, but actually important, for instance).

Shreve's book(s) on stochastic calculus are hard to beat. Friedman's The Elements on Statistical Learning is pretty good, but will require the constant use of google. My background is more applied math (as compared to quant finance), so most of the books/papers I worked off of are "outside" of math finance.

For topics:
-stochastic calc (for option pricing) - this is a rabbit hole that will no doubt lead you to the troubles of dimensionality and expose you to monte carlo/variance reduction techniques. A good paper to look at here is Longstaff Schwartz
- I would recommend looking into topics in linear algebra/matrix analysis. You may not really need this, but interesting things do pop up in other problems when you have this background.
- Time series is definitely a big one. Understanding the behavior of time series data and some of the important models.
- There are like 4-5 fundamental papers to read for math finance regarding Modern Financial/Portfolio Theory
- Probability is important to understand, but not that much fun to study. I would definitely know the common probability questions that are asked in interviews (dice, cards, whatever). Also understanding what expectations/moments/whatever on a fundamental level is pretty important.
If a corner of finance starts to really interest you, then take the time to better understand it (this is where talking to people really comes in handy). For example, if convertible bonds seems interesting, looking into how the market handles them. You need to know all of the general quant topics for interviews, but knowing a lot about some super specific asset class or product (from a job or project) gives you a lot to talk about and makes you sound a lot less naive.

 

[VigQuest]

Shreve's book(s) on stochastic calculus are hard to beat. Friedman's The Elements on Statistical Learning is pretty good, but will require the constant use of google. My background is more applied math (as compared to quant finance), so most of the books/papers I worked off of are "outside" of math finance.

For topics:
-stochastic calc (for option pricing) - this is a rabbit hole that will no doubt lead you to the troubles of dimensionality and expose you to monte carlo/variance reduction techniques. A good paper to look at here is Longstaff Schwartz
- I would recommend looking into topics in linear algebra/matrix analysis. You may not really need this, but interesting things do pop up in other problems when you have this background.
- Time series is definitely a big one. Understanding the behavior of time series data and some of the important models.
- There are like 4-5 fundamental papers to read for math finance regarding Modern Financial/Portfolio Theory
- Probability is important to understand, but not that much fun to study. I would definitely know the common probability questions that are asked in interviews (dice, cards, whatever). Also understanding what expectations/moments/whatever on a fundamental level is pretty important.
If a corner of finance starts to really interest you, then take the time to better understand it (this is where talking to people really comes in handy). For example, if convertible bonds seems interesting, looking into how the market handles them. You need to know all of the general quant topics for interviews, but knowing a lot about some super specific asset class or product (from a job or project) gives you a lot to talk about and makes you sound a lot less naive.

Thank you once again for such a well thought-out response. It's such a coincidence that you mentioned Friedman's Elements of Statistical Learning, as that just happens to be the selected textbook for the Modeling and Machine Learning class that I am currently taking! I will definitely look into the other topics and resources that you mentioned.

For the 4-5 papers I should read for the fundamentals of math finance regarding Modern Financial and Portfolio Theory, where might I find these papers?

 

[jarryds]

Thank you once again for such a well thought-out response. It's such a coincidence that you mentioned Friedman's Elements of Statistical Learning, as that just happens to be the selected textbook for the Modeling and Machine Learning class that I am currently taking! I will definitely look into the other topics and resources that you mentioned.

For the 4-5 papers I should read for the fundamentals of math finance regarding Modern Financial and Portfolio Theory, where might I find these papers?

I don't remember the titles, but a quick google search will give you what you need. You are looking for names like Fama, Black, Scholes, Markowitz