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.

 

[marcusaurelius]

the chips are highly stacked against MFE students (because of how early the process starts)

Hi @Igna, I truly appreciate you posting here.

Here are a few questions:

1) If you are comfortable saying, and if it does not lead closer to your identity, which master's did you end up doing?
2) I have a sense of what you are suggesting with the passage above. Are you suggesting that MFE students are at a disadvantage because recruitment starts even prior to them even enrolling in a program? And since incoming researchers are mostly picked from interns, MFE students do not have the opportunity to hop on the proverbial "train" on time?
- If such is the case, what would an incoming MFE student have to do in order to realign with the recruitment process?
- In tandem, would there be something you would have done differently when recruiting, assuming your master's had a timeline similar to that of most MFE programs?
3) What are the skills you wish applicants would be stronger in?

Best,
M.A

 

[khoatran]

First of all, thank you so much for providing such a golden opportunity for students here to ask questions. I'm currently at the end of my MFE program and am looking to begin my career. I'm interested in the systematic investing/trading area. I have much to learn so I plan to continue learning once I get my full-time offer. What advice would you give students like me to get to work on your team/ to your level?

 

[bisman]

So a person doing a Master in Applied Maths at the University of Colorado, what would you suggest as my course of action to get into the quant industry? I know I am not from an Ivy League, and breaking into the industry would not be easy. What would you suggest to me?

P.S : Thank yous so much for taking out the time

 

[Igna]

Hi @Igna, I truly appreciate you posting here.

Here are a few questions:

1) If you are comfortable saying, and if it does not lead closer to your identity, which master's did you end up doing?
2) I have a sense of what you are suggesting with the passage above. Are you suggesting that MFE students are at a disadvantage because recruitment starts even prior to them even enrolling in a program? And since incoming researchers are mostly picked from interns, MFE students do not have the opportunity to hop on the proverbial "train" on time?
- If such is the case, what would an incoming MFE student have to do in order to realign with the recruitment process?
- In tandem, would there be something you would have done differently when recruiting, assuming your master's had a timeline similar to that of most MFE programs?
3) What are the skills you wish applicants would be stronger in?

Best,
M.A

Hi MA,

1. I ended up doing applied math. It was a program that where I could place out of 2 required courses with my prior coursework so that I could take more electives. Out of all the classes, I ended up taking 4 pure/applied math classes, 5 financial math and 4 other classes in CS/stats/ML (I took 1 more class than required to graduate)

2. For us (and probably to some extent with other competitors based on looking at their intern recruitment page), undergrads and masters (not just MFEs but that's where we get most of the master's applicant pool form) are in the same pool and we start interviewing in October. Since most master's students barely start their programs they could be caught off guard by the aggressive timeline. Also most undergrad applicants have had the role on their radar for a while so they've been preparing for the application (research/project experiences, getting get from their career counselors etc) well ahead of the summer. S
A few points that the more successful candidates have mentioned to me:
a. Some extra bit of prep is warranted. Career counselors have good resources in term of the fundamentals (probability/stats/maths/programming/etc at an advanced undergrad level) that you'd need to know so I'd make sure to get that list and be prepared. You are graded on the process in addition to the solution, so it's good to get into the groove of thinking out loud.
b. If you are switching from another field into quant, then I would brush up on just the very basic finance/econ concepts.
c. A well thought out narrative of why you are doing the master's degree is good regardless of whether you have a target career trajectory. Candidates are able to convince me better to give them a shot at a full time offer if everything else being equal and you sound more confident about your choices.

I have been pushing for a longer first round phone/zoom screening with candidates. In addition to testing the basics, I would reiterate some of the things that would be tested for (nothing surprising here mostly fundamentals) and could up at in conversations at a potential super day (toy problems, what if scenarios with their projects that seeks to have a deeper conversation--not specific questions but a taste of what they can be like). Ultimately, I'm trying to find people who can introspect and think in addition to the basics.

3. Implementation. This is really the more practical side of writing code that simply has to come from experience. The internship is a 8-10 week intensive process where you are expected to deliver a well thought out product at the end. Most of where I see people struggle is that they are taking up so much time to implement something (or run something with highly suboptimal implementation that just takes forever) that they just didn't have enough time to let the results and the process of making an inference sink in.

 

[Igna]

First of all, thank you so much for providing such a golden opportunity for students here to ask questions. I'm currently at the end of my MFE program and am looking to begin my career. I'm interested in the systematic investing/trading area. I have much to learn so I plan to continue learning once I get my full-time offer. What advice would you give students like me to get to work on your team/ to your level?

Of course

I think some qualities that make a good experimental scientist make a good quant (researcher). Curiosity, detail oriented, creative and critical thinking are all important. As we are in the business in making returns, as you mature in your career I would highly recommend giving some thought to what is your unique contribution to the alpha/top line revenue (and if you are not quite there yet what would you want to be your contribution). Compensation and having a good answer to that question are usually correlated, but earlier on in your career I would recommend putting more weight on self development if a tradeoff has to be made.

Best of luck with your career!

 

[UnsureUndergrad]

1) Would you recommend practicing Leetcode for Quant Research interviews? And if yes, to which extent would I be expected to be able to solve problems?

2) How much "advanced" machine learning is ultimately used in the industry or is it mainly just "simple" linear regression?

Thanks a lot!

 

[Igna]

So a person doing a Master in Applied Maths at the University of Colorado, what would you suggest as my course of action to get into the quant industry? I know I am not from an Ivy League, and breaking into the industry would not be easy. What would you suggest to me?

P.S : Thank yous so much for taking out the time

I personally have an applied math master. Some of my early successes in my career were because I was able to implement ideas better and faster because of the applied math training--and these skills are sorely needed everywhere. Having said that, I concede that there is strong brand name bias in the industry.

At this point you should give some thought as to what kind of quant you'd like to be:
1. buy side (probably work on pricing models)
2. sell side (more likely to be data science)
3. researcher (some intuitions about the market is nice)
4. developer (make thing run faster, better, more scalable)
...this is not an exhaustive list, but I'm pointing this out so that you can play to your interests and background.

Definitely cast a wide net and put your best foot forward when you apply for internships/jobs. Take any interview opportunity as a learning experience and don't be discouraged by rejections. If you break in out of school then congrats!

If you didn't land a desired quant role out of school, then breaking in as a quant developer with a few years of experience under your belt would be the easiest way given your training. The path to your "dream" role is not going to be linear, and you might even change your mind as to what really resonates with you at some point. So keep an open mind and never stop learning. Strive to do the best in your role wherever/whatever it may be so that you can accumulate enough trust capital so that people will let you try out something else. My advice above also applies: ultimately quants want to make returns, ask yourself once in a while what is your unique advantage to help your firm (or a potential employer) to generate more alpha/increase top line revenue.

Best of luck with your career and breaking into quant!

 

[Igna]

1) Would you recommend practicing Leetcode for Quant Research interviews? And if yes, to which extent would I be expected to be able to solve problems?

2) How much "advanced" machine learning is ultimately used in the industry or is it mainly just "simple" linear regression?

Thanks a lot!

For researchers, we don't do much LC style question. Any analogous coding questions might be drawn from LC easy at worst. Experience might vary depending on the firm, but we don't believe LC style questions are applicable to what we do in research.

As far as ML goes, usually the closer you are to feature engineering the more ML gets used, and close to actual trade generation the less it gets used. Take text data as an example, you could transform it into bag of words and apply linear regression but modern neural networks are just so much better. Or if you are trying to predict an event, one would be insane to ignore interactions and nonlinear effects between your predictors. At the end of the day, applying ML in trading takes discipline and insights--I would say, with some bit of prejudice, that most of the articles on something like Medium.com that apply ML to some sort of trading application get some aspect of it wrong that it would not be useable in an institutional setting. And if ML ever comes up in a conversation at our interviews, we looks for more insights and practicality rather than just knowing the latest and greatest algorithms.

 

[kitkat]

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: 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.

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 MFE 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.

hey Igna,

Thanks for doing this!

As a 30+ yrs old engineer who has been working in a non-quant engineering job sector (mechanical engineering) looking to transition to quant in the next 3 years, I feel I am not as competitive as those 20+ bright talents who started in quant from the beginning.

On one hand, I am pretty confident with my quant/coding background from years of practice (PhD, fulltime working experience as a research engineer, etc) , but on the other hand, I feel there isn't much runway ahead because I am starting late. Could you possibly share some thoughts/observations on the career path for ppl like me? Very much appreciated!

Thank you!

 

[Igna]

hey Igna,

Thanks for doing this!

As a 30+ yrs old engineer who has been working in a non-quant engineering job sector (mechanical engineering) looking to transition to quant in the next 3 years, I feel I am not as competitive as those 20+ bright talents who started in quant from the beginning.

On one hand, I am pretty confident with my quant/coding background from years of practice (PhD, fulltime working experience as a research engineer, etc) , but on the other hand, I feel there isn't much runway ahead because I am starting late. Could you possibly share some thoughts/observations on the career path for ppl like me? Very much appreciated!

Thank you!

Hi kitkat, thank you for your question. I'm going to dm you since some of the suggestions are more case specific.

 

[kitkat]

Hi kitkat, thank you for your question. I'm going to dm you since some of the suggestions are more case specific.

My appreciation goes beyond what words can describe! Thank you Igna. Truly appreciate your help!

 

[Andy Nguyen]

Thanks @Igna for taking the time to share your knowledge with our community. Much props to you.
I hope prospective applicants take the opportunity to learn as much as possible. This is the type of mentors you need to land you a job.

 

[Jiamin Li]

Hi @Igna

I was wondering what would happen to workers in the quant field if they cant generate a good trading strategy?
I understand that the quant business is all about earning money using programming and maths, and better researchers have more insights and chance of success for alphas. But I also understand that the construction of a good trading strategy has a lot to do with luck. Therefore, there will exists people to tried very hard but cant found good alphas...
I've heard that most companies will tolerate workers for 0.5-3 years before they fire people for not being able to earn money...right now I'm a little worried about this (being a fall23 incoming mfe student) and would like to know what would normally happen to these people and how did everyone deal with it?

Some of the answers I got from people around me would be:
1. quit worrying about this and just first practice leetcode and interviews
2. beginners usually start with more detailed advice before they move on to more abstract fields. I've heard of a "idea list" in some companies where anyone could pick an idea to begin with(this notion is by far the most helpful
3. not working out an idea is ok but you have to explain to the pm to convince him/her why
4. not all groups care about alphas, some care more about hedging their portfolios and minimize risks

But anyway, I've seen people getting fired and I definately dont want to be the next one... while I love this field, I would like some more certainty. So can I ask how people usually dealt with this? and if people are getting fired in their jobs, what could be the red lines that they stepped on?

Thank you for your generosity!!

 

[aryansrivatsava]

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: 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.

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 MFE 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.

I'm aspiring to become a quant researcher in a buy side firm. I am an international student pursuing masters in computer science at george mason university. Right now I have one year left for graduation I have good understanding of machine learning, natural language processing and mathematics. Can you Please guide me how to get interviews for top tier firms and I have been googling a lot and couldn’t find a proper resource which can help me out. I recently came across the "wallstreetquant" bootcamp designed by the quants from wall street. The bootcamp is for three months and the price is around 4000$. I don’t understand if that is worth the amount. I am not able to figure out what skills and projects should I gain and work on, I want to master building the trading strategies, but don’t know a platform where I can learn them. I request you to please invest some time to guide me such that I get enough intuition on how everything related to quant works and I can start working on my profile.

 

[cycbill]

Hi @Igna ,
I've been working as a quant on the sell side for a few years, and I always wanted to try opportunities on the buy side. As a sell-side quant, as you mentioned, I mostly focused on derivatives pricing models, familiar with stochastic calculus and numerical methods, and use C++ and some other languages for implementation. But I am not very familiar with ML and complex statistics. I use Python also but more for prototyping and scripting for test automation, not for ML usages.

Could you please provide some opinions on:
(1) For a sell-side quant, what skills do you think buy-side company is looking for when they want to recruit sell-side quant? Or what aspects do sell-side quant could bring to buy-side company for their alpha/revenue generation?
(2) This question might be a bit similar to the first one. But what type of quants in buy-side would you think sell-side quant would fit in?
(3) What areas would you think a sell-side quant like me should focus on or prepare for when they seek for buy-side opportunities?
(4) There are some companies do option market making or use option in alpha strategies. But do you think the derivatives pricing knowledge would be critical in this cases or knowledge about market structure, statistics or other technics are more important?

Thank you for your generosity!!

 

[LINKSKI]

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.

Thank you very much for this opportunity below is my background and questions.

Background

I am in Sixth form (Y12) in the UK . (So Grade 11 US). I study Mathematics, Further Mathematics, Physics and Computer Science

I recently learnt about this quant field and it interested me because I have always like math's, I started programming a few years ago really enjoy it and I recently started learning to trade which has exposed me to the financial markets and basic economics which I find interesting. So now I'm thinking a job in the quant sector would be amazing.

It is around the time now in the UK where we are being told to think about University and our careers so I would like some advice on what I should study.

I am also interested in electronic engineering in like robotics and devices

I have found different courses which I would be happy to study but none of them really have all of my interests because I would like to keep my options open.

These are links to some of the courses: electronic and electrical engineering, computing with finance and management, electronic and information engineering.

Questions

1. Is it best to study a degree with 1 job sector or career in mind or is me trying to keep my options open a good idea?
2. Can I get into the quant sector by doing engineering degrees like I have linked above and then doing a financial engineering masters?
3. What are the best courses I can study to get into the quant sector?

Thank you!

 

[Igna]

Hi @Igna

I was wondering what would happen to workers in the quant field if they cant generate a good trading strategy?
I understand that the quant business is all about earning money using programming and maths, and better researchers have more insights and chance of success for alphas. But I also understand that the construction of a good trading strategy has a lot to do with luck. Therefore, there will exists people to tried very hard but cant found good alphas...
I've heard that most companies will tolerate workers for 0.5-3 years before they fire people for not being able to earn money...right now I'm a little worried about this (being a fall23 incoming mfe student) and would like to know what would normally happen to these people and how did everyone deal with it?

Some of the answers I got from people around me would be:
1. quit worrying about this and just first practice leetcode and interviews
2. beginners usually start with more detailed advice before they move on to more abstract fields. I've heard of a "idea list" in some companies where anyone could pick an idea to begin with(this notion is by far the most helpful
3. not working out an idea is ok but you have to explain to the pm to convince him/her why
4. not all groups care about alphas, some care more about hedging their portfolios and minimize risks

But anyway, I've seen people getting fired and I definately dont want to be the next one... while I love this field, I would like some more certainty. So can I ask how people usually dealt with this? and if people are getting fired in their jobs, what could be the red lines that they stepped on?

Thank you for your generosity!!

(crazy week and had a lot of other stuff going on)

This is an interesting question and very much depends on the firm/PM that you will be working for. It's always rough in down years. But generally, I don't think that there's too much of an expectation that you are going to be delivering strong alphas within the first year out of school (if you have a PhD there might be a bit more expectation). This goes hand in hand with your second point. As a junior researcher, you will usually be guided by someone more senior. For us, coming out of undergrad/master's it's assumed that you'll have built up enough of a research sense after a year or so on the job that you are given more autonomy. At that point, you should hone your skills so that it's less a matter of luck--this has to do with having good intuition and meticulous execution.

However, if there are deficiencies in someone's ability to perform the job in the longer term, I would point it out to him/her. If there's no visible effort to improve then we would consider parting ways. Part of this has to do with what your 3rd point talks about. You should strive to understand exactly what you are trying to do and why it's (not) working. Even if it's just following directions from a more senior researcher, not paying attention to the finer details and thinking critically are huge red flags.

 

[Igna]

Hi @Igna ,
I've been working as a quant on the sell side for a few years, and I always wanted to try opportunities on the buy side. As a sell-side quant, as you mentioned, I mostly focused on derivatives pricing models, familiar with stochastic calculus and numerical methods, and use C++ and some other languages for implementation. But I am not very familiar with ML and complex statistics. I use Python also but more for prototyping and scripting for test automation, not for ML usages.

Could you please provide some opinions on:
(1) For a sell-side quant, what skills do you think buy-side company is looking for when they want to recruit sell-side quant? Or what aspects do sell-side quant could bring to buy-side company for their alpha/revenue generation?
(2) This question might be a bit similar to the first one. But what type of quants in buy-side would you think sell-side quant would fit in?
(3) What areas would you think a sell-side quant like me should focus on or prepare for when they seek for buy-side opportunities?
(4) There are some companies do option market making or use option in alpha strategies. But do you think the derivatives pricing knowledge would be critical in this cases or knowledge about market structure, statistics or other technics are more important?

Thank you for your generosity!!

When there is optionality (either explicit or implicit) in the product a PM wants to trade, they like to build up some way to price it in-house. This could either be used to identify mis-pricing or to hedge their portfolios. So there is a direct transference of expertise from one side to the other. The level of commonality depends on the specific instrument.

The nature of an alpha focused role is probably different from a typical sell side role in the sense that it's much more about trying to predict the future and positioning oneself in anticipation of it. That's why you see some requirements around ML (potentially better predictions) and statistics (are you sure you have better predictions?).

If you want to transition, you should ask yourself what is it about the buy side firm that really attracts you. Would continuing to create pricing models be something that you'd want to do? If so then try to be the top expert in what you are doing now. Would you want to be closer to alpha generation? Then that would require more work to demonstrate to potential hiring managers that you have the ability to do it--perhaps by getting closer to some sort of a signal research role internally, or collaborate with researchers in publications etc (if these possibilities exist in your current company).

As a last point, quantitative portfolio management is also really a stochastic control problem, so you can draw some additional parallels. However, only a subset of PMs actually model things out as a stochastic control problem since there is that gap in skillset between buy and sell sides.

 

[Jiamin Li]

Thank you Igna for your answer! Big appreciations!!!