I'm a senior buy side quant researcher. AMA

Hi all,

As the title says, as prospective students should be making their decisions, I would like to use this platform to interact with students who might be interested in the career path.

Who Am I: I’m a senior quantitative researcher working in systematic equities. I’m what some might call a “full-stack” quant leading a team on the entire pipeline from data exploration to generating the trades that we want to do (not the actual execution though). I have been lurking on Quantnet for a few years. I did not pursue an MFE, but I did a related master’s degree and my choice for the school was informed by the rankings here. For anonymity reasons, this is not my main account.

About My Company: we are a billion$+ quantitative hedge fund dealing with all asset classes at various horizons. Our incoming researcher pool are heavily drawn from previous year’s interns.

Why Am I doing this (edited): thing can be a bit opaque from the other side, and I've enjoyed interacting with members both in this thread and in DM's. Would like to open a forum in an anonymous manner to give some perspective.

Ground rule: as long as there’s no question that can be used to identify me or my company, I’m ok to answer. I'm not going to endorse any particular schools/programs. Also, everything that I say is of my own personal opinion and there might be people with same title/responsibilities as me that have differing views. At the end of the day, I hope that what I'm saying is not totally idiosyncratic.

I'm on US Eastern time, and have a full time job so please understand if I don't answer immediately.

Pre-edit:
Why Am I doing this: I’ve been involved in our recruitment process of the last few years at all levels (undergrad all the way to experienced). As far as the process goes at our company, the chips are highly stacked against MFE students (because of how early the process starts). I personally would like to see more well prepared MFE graduates in our ranks, and I’d like to use this platform to both help students who are interested in that career path and collect some data points for my information.

 

[MikeLawrence]

First of all I think you are underselling yourself, you are at least a charmeleon so let’s put some wings on you.

 

[another]

Hi! Thank you a lot for doing this. How does the career track of Quant Researcher as compared to Quant Trader? I heard that both of them do some predictive analysis to forecast market movement/price change. What are some of the pros/cons of each role in your opinion? Appreciated!

 

[Phi-quant]

Shout out to @Igna. Thank you so much for taking your time to help us out. I learned a lot already reading your answers to other people. Thank you so much.

I’m sorry about the following long post. The next 4 paragraphs are my background. You can probably skip some if you just wanna see my questions.

I am a 6th PhD student studying physics at a non-target university. I also have a PhD minor in statistics, with courses including theory of statistics, machine learning, statistical computing, fundamentals of optimization. Math has always been my favorite subject. I really liked quantitative finance, and want to pivot my knowledge and skills from math and physics to a quantitative researcher.

My research area is condensed matter physics and involves discovering and characterizing novel quantum magnetic materials, that could be useful in both fundamental understanding of physics and application in technologies, such as quantum computing. That being said, my research is mostly experimental (crystal growth, material characterization (XRD, SEM, AFM, Raman), magnetization measurement and has almost nothing to do with quant. We also do some calculations that involves modeling and computations, like band structure and magnon interactions, but we do these by collaborating with other theory groups. I have decent publications, 10 articles so far but I am only afraid my experimental papers are not appealing to quant people. That is, my research topic itself has almost nothing to do with quant finance.

In terms of math and programming, I have pretty good understanding of probability theory, linear algebra and calculus. I have meager understanding and a little experience of implementing machine learning. I have basic programming skills in Python, R and MATLAB. I also know a little bit of C and C++ but I don’t use them in my work. Knowledge of C and C++ is getting rusty. I have never taken a data structure and algorithms course, although I have been exposed to some of those algorithms here and there.

For financial experience, I have traded stocks from 2018 and spent a lot of time on it in 2020. I learned a lot of manual momentum trading. I am good at Mark Miniverni’s momentum trading strategies. I also tried to implement momentum trading strategies in Python and learned a lot. I That being said, I don’t have any formal education in finance. I don’t know much about financial derivatives, pricing theory, or portfolio optimization theory.

I started to know about quant 4 years ago and started doing more research about the quant career from this year. Now, I am aspiring to be a quant, more specifically a quantitative researcher. I started preparing this year from late February and applied 19 positions, all hedge funds. Among them, I got 1 OA which I passed but tanked the interview after two rounds. I realized I wasn’t prepared enough. It’s now mid April so I have about 5 months until mid September before I apply for internship or full time positions. I’m now studying machine learning 4-5 hours a day, mostly working through ISLR and ESL. I want to design a pathway (study & internship plan) to break into the quant career.

I am still working full time in my lab so suppose 4 hours study a day. I have about 5 months, or 600-700 hours. My goals for the next 5 months:

1. Enrich my resume (may be personal projects, research projects with a professor, trading competitions, internships in quant research, quant trading, software engineering, or data science) to get interviews.
2. Have enough knowledge foundations to pass the interviews and get offers.

My questions:

1. Self study plan: if I were to self-study for the next 5 months before I apply for buy side QR roles, what do you recommend me to study, in addition to prob&stat, linear algebra, calculus)? What are absolutely necessary for a quant researcher? What are optional? I have listed possible areas of study based on my research, for your reference.
   1. Optimization in asset management.
   2. Stochastic calculus for finance, Steve Shreve volume 1 and 2
   3. Derivatives markets by McDonald
   4. Data structure and algorithms
   5. Python programming
   6. C++
   7. Machine learning
   8. Mining of massive data sets
   9. Time series analysis
2. Off-summer internships: now, I don’t feel I am ready for QR interviews and there are not many positions available. The chance of getting a summer internship is bleak. Or is it just my illusion and there are actually some available and I should apply? Are there many internship opportunities in the off-summer season?
3. Other internships: If I can’t find a QR internship soon, what’s the next best internship option I should try? SWE or data science? Which one would better prepare me for future quant job applications?
4. Projects: if I can’t find an internship in any of these areas, I would like to work on a project with a professor, which topics do you think would make me stand out?
5. internship after graduation: is it okay to look for internships after graduation? Like I graduate in May in 2024 and do an internship during summer 2024? Or should I apply for 2024 full time positions?
6. Comparative advantages and niche: since you mentioned before, candidates should have strong core competency, comparative advantages and niche. That’s really enlightening so THANK YOU so much. I am wondering what my comparative advantages and niche are / could be. My comparative advantage could be my statistical / math knowledge but I don’t have much research experience like some PhD have their theses on ML or statistics. My niche might be experimental physics? But I don’t know know how that is exactly related to quantitative finance and could differentiate me from other PhDs in this field. Could you gave me some advice?

I really appreciate your time and any inputs. I also appreciate if you can point me to the right resource, if you or someone else have answered some of my questions. Thank you again!

(sorry for the delay, was traveling)

I would put some emphasis on implementation (data structure, algorithms, etc). Usually PhD are given a bit of benefit of doubt, but not being able to implement will be a blemish on the interviews, and will hobble you for the actual internship. At some point before you apply, you might want to learn a bit more about the target companies and the kind of questions that they ask. The breadth of knowledge, if you wanted to cover everything, is very vast. I would then keep the other study more general--ML, stochastic calc, time series--have the theory and some implementation under the belt would be good.

I have seen people with winter internship experience from reputable competitors in our hiring pool, but we don't do it and have no idea how that works. If you can find one, I think it adds some value though the duration is shorter than summer internships.


We look at machine learning engineer profiles for our QR pool (both internship and experienced hire). Normal SWE and data science backgrounds are usually not considered. I think there is just so much heterogeneity in the DS degree and job role that it makes it hard to consistently evaluate.


I would tailor it to the expertise of the professor. I think some high quality research should help your case and a good professor should at least make sure it is academically sound. The problem with some of the academic research is that it is often detached from reality and how the market works, and I would especially recommend avoiding doing some sort of theoretical asset pricing research (e.g. stochastic discount factor related), though empirical asset pricing (e.g. predict returns with ML) is ok but a bit overdone.

I've also seen this but much more so for undergrads who have graduate school plans. If I were you, I would apply for both the internship and full time.

Based on your background, it's not something that we typically see in our PhD pools. I'll take this conversation into DM's and ask you a few more questions and address it there.

@Igna Thank you so much for the previous response. I have learned a lot in the last couple of months.

Following up with previous posts, since now is application season, I have two questions about resume and LinkedIn profile.

I have been teaching in high school (like, Teach for America program) for two years before I joined my physics PhD program. I'm applying for a quant research position.

My research and publications are experimental condensed matter, close to material science. They don't look like theoretical physics or machine learning articles quant shops like (is this really true?).

People say during the first stages of job search, employers are high alert on red flags and looking for reasons not to hire you. I am concerned these two things could be reasons they would reject me. Therefore, I would really like to know your opinions on:

1. Would it hurt my chances of getting an interview if I add my teaching experience on my LinkedIn experience section? I know it gave me very unique perspective and character and shows my dedication to continuous improvement. I value that experience. But I would like to see how real quants think about this. Is this something you would hesitate about? I don't plan to put it on my resume because of space limit.
2. Would it hurt my chances of getting an interview if I put my experimental material science publications in my LinkedIn profile? Should I put them on my resume?

Appreciated!

 

[dgpo]

Hello!

Sorry if this is a bit long!

I currently studying Electrical and Electronic Engineering at a semi-target UK Uni (think Warwick, Manchester, Durham), entering 3rd year, predicted a first class with first class grades in classes like Linear Algebra, Vector Calculus, Differential Equations, Signal Processing and C/C++, and will be taking classes in PDEs and Numerical Analysis. I will also have a ML based 3rd year project (proposed project is on using time series analysis to develop a means reverting strategy for pairs trading so will get more experience). Furthermore I just finished a SWE internship at a top tier investment bank (think GS, JP or MS), with a return offer for another internship.

I am interested in quant finance, as I believe I have the ability and desire to further my maths skills, as well as develop my programming skills and learn more about the markets. I had some exposure to the markets during my internship and I found it absolutely thrilling. The problem is, the best of the best master's courses (i.e. Maths and Finance @ Imperial or MCF at Oxford and basically all the Maths x Finance Masters) only explicitly take maths students, and I'm not sure I'd want to waste my time applying to them as I am almost certain I'll get rejected. I am therefore looking at the following courses that I think have the right blend of math, finance and CS/ML classes (I'll list them in order of my preference):

1. MSc Computational Finance @ UCL
2. MSc Financial Technology/MSc Risk Management and Financial Engineering @ imperial
3. MSc Financial Technology @ UCL (bear in mind the FT and CF courses share almost entirely different core modules, but have more or less the same optional ones)
4. MSc Advanced Computing @ Imperial (I like this because I can choose all my modules, and they are good if I decide to go down the more Quant dev/Algo dev route which is something I am considering, given my background)
5. MSc Computational Finance @ KCL
I am also considering MSc Computational Mathematical Finance at Edinburgh and (if all else fails), perhaps MSc Quantitative Finance from Manchester.

What courses would you recommend I add/remove from there? I'm quite keen on the first 2 and the 4th one, first choice being the UCL CompFin due to the compulsory placement.
Imperial is where the indecision is, my slight preference is FinTech because (in terms of core modules) they still have a good number of maths/stats classes, but perhaps more application and more general skills that would serve me well if I decide to go start my own company for example. I also really like the Financial Econometrics in R/Python module, which isn't on the RMFE course. My only concern is whether Imperial FinTech won’t look as good for quant on my CV/LinkedIn as RMFE, as the mathematical rigour is definitely there, but not as much as RMFE. Then again, my concern with RMFE is that it seems pretty specialist and I might narrow myself down to more middle office quant/risk management positions. I'm stuck here!

Would love to get some feedback!

Thanks a lot!

 

[MMK]

Hello - I have a PhD in engineering (honors) and my thesis was all about statistical modelling using python programming (to keep it brief lol). I have 7 years of experience in the auto industry as system engineer and 1.5 years as a strategy consultant (current job). I am taking online courses to expand my skills about python in trading and finance. I am seeking a career switch to become a Quant Researcher.

A few questions that I have:
1- is my profile a fit to make the switch?
2- What companies (mid tier) do you suggest I should target? Any hiring cycles ?
3- do quant researchers work long hours like investment banking? strategy consultants?

 

[Tapasbrk]

Hi all,

As the title says, as prospective students should be making their decisions, I would like to use this platform to interact with students who might be interested in the career path.

Who Am I: I’m a senior quantitative researcher working in systematic equities. I’m what some might call a “full-stack” quant leading a team on the entire pipeline from data exploration to generating the trades that we want to do (not the actual execution though). I have been lurking on Quantnet for a few years. I did not pursue an MFE, but I did a related master’s degree and my choice for the school was informed by the rankings here. For anonymity reasons, this is not my main account.

About My Company: we are a billion$+ quantitative hedge fund dealing with all asset classes at various horizons. Our incoming researcher pool are heavily drawn from previous year’s interns.

Why Am I doing this (edited): thing can be a bit opaque from the other side, and I've enjoyed interacting with members both in this thread and in DM's. Would like to open a forum in an anonymous manner to give some perspective.

Ground rule: as long as there’s no question that can be used to identify me or my company, I’m ok to answer. I'm not going to endorse any particular schools/programs. Also, everything that I say is of my own personal opinion and there might be people with same title/responsibilities as me that have differing views. At the end of the day, I hope that what I'm saying is not totally idiosyncratic.

I'm on US Eastern time, and have a full time job so please understand if I don't answer immediately.

Pre-edit:
Why Am I doing this: I’ve been involved in our recruitment process of the last few years at all levels (undergrad all the way to experienced). As far as the process goes at our company, the chips are highly stacked against MFE students (because of how early the process starts). I personally would like to see more well prepared MFE graduates in our ranks, and I’d like to use this platform to both help students who are interested in that career path and collect some data points for my information.
View attachment 50598

Hi Igna, I appreciate that you are taking your time to help prospective quants over here. I needed your suggestions on a problem that I am facing. I will be completing my BS in Finance very soon. I have a good understanding of finance concepts. I have felt during my undergraduate course that I perform strongly in subjects which are quantitative in nature and have realised that I am always interested in understanding the quantitative side of finance concepts. Also, I find subjects like statistics and business analytics very interesting. I will be doing an MS in Quantitative Finance from not so well known college after my bachelor.

Looking at the requirements of the quant jobs and people's profiles who have applied to some prestigious colleges, I feel I am already behind in terms of quant and programming subjects as that has not been the prominent focus of my bachelor's. I want to know how I can work towards having a better position in terms of quant knowledge considering that I do not have a strong background for the same in my undergraduate.

Also, I would like to know your opinion about which certification would be better to pursue along with masters, CFA, or FRM. To me, FRM looks more attractive due to its more quantitative approach but still would like to know your opinion.