PhD vs Full Time Quant Researcher

[alittlebear]

Hi all,

I am a senior student who is about to graduate and now feel a little confused about the future. I want to be a top quantitative researcher and my goal is to enter a top hedge fund. I have two options, one is to work after graduating from a master's degree, and the other is to continue to pursue a PhD degree.

I have received some offers suitable for pursuing PhD in Statistics (Uchicago and Duke stat ms) and some offers suitable for pursuing PhD in Finance (Columbia Business School FinEcon ms). If choose to work after graduation, I may need to constantly change jobs to achieve my goals.

My question is:
1. I saw many people continue to study PhD after reading MFE and finishing a decent summer intern, so I would like to ask whether the work of a full-time quantitative researcher is really as interesting as what people thought and do they have a steep learning curve?
2. Whether companys like DE Shaw and Citadel would prefer finance PhD or statistics PhD, or both have the opportunity to get into them directly?
3. What is the salary for PhD at the beginning and the salary for 5 years after MFE graduation? Assume that MFE students worked in big bank / top prop trading like IMC.
4. In the long run, is PhD more conducive to career development?

I have passion in both finance and statistics so PhD degree is not a problem, and I also hope to learn more through PhD training.

 

[tips]

1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great
2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel.
3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm
4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary

however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn

 

[tips]

Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are

1. An ability to pick up new concepts quickly
2. The ability to implement their ideas into production code
3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever.

So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds)

 

[alittlebear]

1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great
2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel.
3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm
4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary

however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn

Thanks for your reply, it is much appreciated!

For my career goal, yes, I do want to start my own firm in the future. I think before doing that going to a top firm is a need to learn their insfracture and also it is a way to be recognized by investors. A question is what do you mean by 'start your own strategy'? I think most quant researchers will become PM one day and have their own book even they do not have a PhD degree, right?

 

[alittlebear]

Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are

1. An ability to pick up new concepts quickly
2. The ability to implement their ideas into production code
3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever.

So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds)

Thanks for the reply!

I think I do have the abilities but what I am facing is a more and more competitive job market. I am not from a top undergraduate school so it's pretty hard for me to find jobs in leading buysides after graduation from MFE. It would be pretty struggle for me if I start to work at sell sides. Also, I think most of the applicants are using interview book and leetcode to 'fit' the requirement of a job, instead of truly master the knowledge in statistics/math, which I think is quite boring. I am in awe of knowledge and also know my deficiencies in statistics, that's why I want to have a PhD degree

 

[monbuchicassie]

Thanks for the reply!

I think I do have the abilities but what I am facing is a more and more competitive job market. I am not from a top undergraduate school so it's pretty hard for me to find jobs in leading buysides after graduation from MFE. It would be pretty struggle for me if I start to work at sell sides. Also, I think most of the applicants are using interview book and leetcode to 'fit' the requirement of a job, instead of truly master the knowledge in statistics/math, which I think is quite boring. I am in awe of knowledge and also know my deficiencies in statistics, that's why I want to have a PhD degree

Hi, I have the same problem as you had. I am a master majoring in statistics and also would like to apply for a PhD program in either Finance or Statistics and we seems have the same career goal. I am curious which program you accepted at last?

 

[TheComplexUnit]

(1) You'll learn more practical skills on the street than you would in any PhD programme. (2) Hedge funds are not 'maths departments'. In other words, you'd be surprised how easy the maths you'll need is. (3) Getting into so-called "top companies" is not necessarily what you want to aim for. Chances are you'll be pigeon-holed into a highly specialised semi-brain dead role. Aim for smaller companies with a start-up attitude. (4) These days, highly skilled developers with a modicum of quant skills are kings. Again: avoid the delusion that epsilon-delta will bring you glory.

 

[tips]

Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us

1. Great c++/ OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers)
2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus)
3. Good research ability
4. attitude

While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm

 

[Daniel Duffy]

What's "SWE background"?
What distinguishes "programmer" from "developer"? It may be an idiosyncratic and have several definitions on who you ask.

I would say most quants of any flavour should be able to test their ideas (in a computer).

 

[tips]

SWE = software engineering

To me, a "programmer" is someone who has a basic to decent grasp of the syntax in some language (i.e knows the syntax for c++/python) enough to be self-sufficient at creating their own models and run backtests given enough time (I.e. a recent undergrad in a non-CS degree with some CS courses, someone who has solely taken a quant net class); A programmer might not understand the cost associated with copying vs pass by reference or the usefulness of move, but would understand enough to implement a basic linear regression given enough time

A developer is someone who can take that model and architect the libraries / packages / whatever else is needed to productionize that model (keyword here is architect).

Being a good architect might mean having a solid grasp of design patterns, a deep enough understanding of the language to understand trade offs of using certain data structures / keywords, the skill to write really good unit tests, and an ability to think about the future of any design choices made (i.e. if part of your model requires implementing something which should be generic to future models, are you implementing it in a generic way so another researcher can use it?) - This is usually someone who at bare minimum the credentials of a "programmer" but also has some work experience and / or a computer science degree from a decent program that teaches these skills.

Finding a "strong developer" who has good quant skills is pretty hard IMO, and is part of what makes it so difficult to get a job as a quant researcher at one of the top prop shops / hedge funds that specialize in fully automated trading.

Agreed fully that there are many definitions, and many different ways to satisfy the criteria I listed, just how we distinguish when hiring at my firm.

 

[monbuchicassie]

By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need?

 

[Daniel Duffy]

Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background

Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me.
It takes [7,20] years to really learn software design and apply it to anything.

Software and programming is a skill to be learned. Many underestimate the task.

The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall.

 

[tips]

Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background

Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me.
It takes [7,20] years to really learn software design and apply it to anything.

Software and programming is a skill to be learned. Many underestimate the task.

The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall.

Agreed, which is why sometimes PhDs are still better hires because they’ve had simply more time to hone their skills (though as mentioned earlier this isn’t always true)

 

[PepeQuant]

Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us

1. Great c++/ OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers)
2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus)
3. Good research ability
4. attitude

While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm

Totally agree! Nowadays skillful dev with medium quant skills rules.

I would like to add one more skill probably for more longer term success: good intuition. What distinguish among those quants is the their ability to interpret and deep understand their models for practical use (such as how to reflect users' or even their own systematic view of market dynamic). Often I found math knoweldge and market experience really helps on those situations.

 

[tips]

By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need?

To me, a quant analyst is really vague because its a term used throughout the job market, not just in finance. In most cases it's someone who is more of a "programmer" with basic to medium quantitative skills, basic to medium research ability; In very rare circumstances, they might do the same work as a researcher (like 15% of the time). Typically, these candidates have at least an undergrad, given that it is typically junior most of these candidates are fresh out of undergrad or a masters program

A quant researcher is someone who is more of a "dev" with medium to strong quant skills, strong research ability (aka what most users here typically think of as a quant), though on occasion some jobs might actually be closer to a quant analyst (like 15% of the time, not an exact number this is just a guess based on my intuition); Typically, these candidates have graduate degrees (masters+ some work experience, PhDs, though strong undergraduate candidates can do this as well)

Given the vagueness, you should always make sure to ask what the job/title means to whatever firm you're interviewing with since the definitions can be variable. The work of a researcher at an old school firm vs a fully automated firm is very different

 

[monbuchicassie]

To me, a quant analyst is really vague because its a term used throughout the job market, not just in finance. In most cases it's someone who is more of a "programmer" with basic to medium quantitative skills, basic to medium research ability; In very rare circumstances, they might do the same work as a researcher (like 15% of the time). Typically, these candidates have at least an undergrad, given that it is typically junior most of these candidates are fresh out of undergrad or a masters program

A quant researcher is someone who is more of a "dev" with medium to strong quant skills, strong research ability (aka what most users here typically think of as a quant), though on occasion some jobs might actually be closer to a quant analyst (like 15% of the time, not an exact number this is just a guess based on my intuition); Typically, these candidates have graduate degrees (masters+ some work experience, PhDs, though strong undergraduate candidates can do this as well)

Given the vagueness, you should always make sure to ask what the job/title means to whatever firm you're interviewing with since the definitions can be variable. The work of a researcher at an old school firm vs a fully automated firm is very different

thanks a lot