2025 Buy-Side Quant Job Advice

Don't ask me for advice, just read this

Preamble: I get a regular inflow of 2–3 emails per week from young-ish people asking for career advice (or, more nakedly, for a job). I ask them: have you read my short document on career advice, which I have posted everywhere? Nine out of ten answer no. This is another attempt to simplify everyone’s life.

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Let’s start with some cautionary remarks. You are probably asking the wrong person, because (a) I wanted to do physics or write books at your age, not finance, and I moved into this industry exceptionally late in my career; and (b) all my decisions in life, including work-related ones, have been strictly non–revenue-maximizing. So you can stop reading now—or endure 15 pages of advice.

Now, let’s really start. If you are of legal age and are into it, now is an excellent time to grab a stiff drink.

The Outside View

The first question I would like to address is: what are my chances of getting a job as a quant researcher? The “buy side” is the segment of the financial industry comprising firms that buy securities for their own accounts or as agents for third parties. These include private equity firms (e.g., Blackstone), venture capital firms (e.g., Sequoia), asset management firms (e.g., BlackRock), pension funds (e.g., CalPERS), family offices (e.g., Soros), hedge funds (e.g., Citadel), and prop trading firms (e.g., Citadel Securities).

All of these have quants on staff, but there are important differences. Every firm is a bit like Orwell’s Animal Farm: all employees are created equal, but some employees are more equal than others. In private equity and venture capital, quants are not at the core of the business, and in a good portion of asset managers, pension funds, and family offices, quants are not working on the most exciting problems. You probably want to begin your career in a place where quants are first-class citizens and are actually using their brains.

I will focus mostly on hedge funds and prop trading firms; they are also the only firms I know from direct experience. The first thing to realize is how tiny this segment is. The assets under management of the hedge fund industry are, give or take, $6T, out of roughly $125T of worldwide equity market capitalization. Prop trading firms are a rounding error relative to that $6T. The order of magnitude of hedge funds operating globally is about 30,000, but the distribution is heavy-tailed. Since inception and as of 2023, the top 20 hedge funds have generated about 19% of total profits (out of perhaps 100,000 hedge funds ever in existence). In the past three years, the top three hedge funds (Citadel, Millennium, and D. E. Shaw) have generated 38% of total PnL.

The multi-manager hedge fund platforms (more on them later) manage only about $500B in total (roughly 10% of hedge fund AUM), but have been responsible for perhaps 50% of the sector’s PnL in recent years. They also employ about one quarter of all hedge fund employees.

All of this should provide sufficient evidence for the following three points:

1. Hedge funds and prop trading firms are a niche sector.

2. Within this sector, the firms that are profitable and have a sizable headcount are a tiny niche.

3. And, of course, quants are a sliver of the headcount.

Regarding the last point: investment personnel in a hedge fund or prop trading firm typically account for 30–50% of total headcount. Depending on the focus of the firm, quants make up 30–100% of that group. Most importantly, job openings depend on firm growth and employee turnover; a reasonable ballpark is 5–10% per year.

So, which are the very profitable hedge funds and prop trading firms that will hire at least a dozen quants every year? Note that the distinction is sometimes blurry, because some prop trading firms manage outside capital, and some hedge funds invest in or have opened prop trading divisions. A non-exhaustive list, with no pretense of completeness:

• Akuna

• Arrowstreet Capital

• Balyasny

• Citadel

• Citadel Securities

• CTC

• DE Shaw

• DRW

• Engineers’ Gate

• Exodus Point

• Flow Traders

• GSA

• Headlands

• Hudson River Trading

• IMC

• Jane Street

• Jump Trading

• Millennium/WorldQuant

• Optiver

• PDT

• Point72/Cubist

• Quadrature

• Qube Research Technologies (QRT)

• Radix

• Renaissance Technologies

• Susquehanna (SIG)

• Schonfeld

• Squarepoint

• TGS

• Tower

• XTX

• Two Sigma

• Virtu

• Voleon

• Verition

• Walleye

• Weiss Asset Management

• Wolverine

And then there are many smaller firms that you may want to consider: Five Rings, Old Mission, Maven, etc. It’s a very long, thin tail.

I would guess that the total number of investment professionals in this space is around 15,000. So maybe 7,000 quants, and a demand of roughly 700 quants per year. That includes everyone—from alpha research, to portfolio construction, to data analysis, to execution, to risk management.

On the supply side, there are a few sources:

1. The top echelon of fresh STEM graduates.

2. MS programs in Financial Engineering (MSFE) from Baruch, Cornell, Chicago, Columbia, NYU, CMU, etc. MBAs occasionally get quant jobs, but this is much rarer.

3. Sell-side employees on the verge of a nervous breakdown and trying to switch sides. I have no idea of the exact number, but it’s clearly large, since about half (~20,000) of my current LinkedIn contacts fall into this category.

4. Young employees already working as buy-side quants.

Of these four groups, the first two are in particular need of guidance because of their total lack of industry experience. Some considerations:

• For undergraduates: the chances of receiving an internship in the US are exceedingly low for non-US residents or non-F-1 visa holders (i.e., international students at US colleges). You will likely have to start with an employer in your local economic area (your country, or the EU for EU residents). Some of you will be able to upgrade and eventually move to the US through your employer. Alternatively, you can pursue option #2.

• For MSFE students: the MSFE is often perceived as the one chance to get started in finance in the US. Is that true? There are more MSFE programs than Taylor Swift breakup songs in this world, so concern about market saturation is legitimate. Since I don’t trust school sources or “independent” advisors (incentives), I ran a poll on X and LinkedIn. I received a total of about 6,000 responses—not a small sample size, even if possibly a biased one. Take a look at the answers and make up your own mind. My reading of the data, partly informed by my hiring experience, is that the best programs do help students find jobs. And which programs are those? I can’t help much here. I would say that geography matters: NYU, Cornell, Baruch, Princeton, and Columbia have an advantage because of their proximity to New York–based financial firms.

Firms like to boast about the selectiveness of their programs while, at the same time, advertising their outreach: friendly, yet out of your league. Looking at advertised acceptance rates is misleading, though. Citadel and top prop trading firms often cite a job-offers–to–submitted-resumes ratio of 0.2%. True, but also false, because they count every single submitted résumé. I have personally screened maybe a couple thousand résumés, some of which were masterpieces of surrealist literature. If you are a piano player in a brothel, know that your chances are unfairly slim—but rest assured that your résumé was still counted in the denominator.

The Inside View

Before the interview

These are the steps you should take in order to get an interview. There is a great deal of anxiety and drive around this process, since it is very competitive. I have had a few high school juniors inquire about compensation packages and non-competes, which is borderline scary. That said, I would offer the following practical advice.

• Follow target firms on social media: LinkedIn and X, and perhaps a few Reddit channels (r/quant, r/algotrading), Discord servers, or similar forums. Follow their job postings and apply in a timely manner when they announce internships, externships, winterships, or dog-and-pony shows.

• Research your prospective employer the way you would research a company you invest in. Be prepared, and know specifically what they do and what they are good at.

• Show up at on-campus recruiting events, if they take place at your school, in your town, or at a conference you will attend (e.g., NeurIPS). Attendance gives you brownie points. Bring a résumé. Back to point #2: ask informed questions about the company.

• Participate in a few extracurricular activities, such as a local investment club. I personally find these clubs rather sad, and the people attending them slightly check-the-box robotic (the kind of people who… follow my advice here? Sorry), but maybe I am wrong and they are actually useful.

• Subscribe to Matt Levine’s “Money Stuff” newsletter; read his past articles as well. They are informative, funny, and have aged well. They are free. They are just too long.

• Read a few entertaining books for fun and profit. With the exception of Bernstein and Zuckerman, these are all first-hand accounts:

  • My Life as a Quant by Derman

  • Against the Gods by Bernstein

  • Red Blooded Finance by Brown

  • The Education of a Speculator by Niederhoffer

  • The Man Who Solved the Market by Zuckerman

  • A Man for All Markets by Thorp

  • Fooled by Randomness by Taleb

  • The Alchemy of Finance by Soros

People ask brain teasers for a couple of reasons. First, to probe basic modeling and math skills. Second, because they serve as a focal point: everyone knows they are a likely topic. So I am not testing your intrinsic ability to solve a puzzle, but your ability to learn how to solve puzzles. There is a pattern to puzzles, and it can be learned. Work through all of Peter Winkler’s books (Mathematical Puzzles and Mathematical Mind-Benders). Various firms, including Jane Street and IBM, also have puzzle sites.

Coding tests: I am not an expert here and have never taken a coding test myself. I think that at least going through the advanced tests on Codility, CodeRank, and HackerRank may help. There must be books out there; suggestions welcome. A recommendation I received from Lennart Finke (a graduate student from Germany) is:

• Elements of Programming Interviews by Aziz, Lee, and Prakash

• Competitive Programming, 4th ed., by Halim and Effendy

• Classic Computer Science Problems in Python, by Kopec

Applied probabilistic modeling and statistics are very important skills to have. Physics is still a good major to hire from, because it is a model-based discipline rather than a technique-based one, and you will be exposed to many models. Take classes at the MS level. Read at least the following books:

• All of Statistics (both volumes) by L. Wasserman

• Applied Probability Models by S. Ross

• Convex Optimization by S. Boyd and L. Vandenberghe

• Numerical Linear Algebra by Trefethen and Bau

• Linear Algebra and Learning from Data by G. Strang

• How to Solve It by G. Pólya

Note that I do not recommend any finance books. You will learn that on the job.

• If you pass the coding tests and get interview offers, try to interview with the least desirable places first, in order to gain real-life experience.

• If you get an internship offer, be aware that you will not really work during the internship. You will be subjected to a well-paid, seemingly fun, but effectively unproductive ten-week-long interview. As in any measurement process, as the firm sizes you up, it will also be your chance to size up the firm. Have fun and learn as much as you can about the firm and whether you would like the job, but look beyond appearances. The internship is not representative of your future job. The evaluation you receive will be a noisy signal, depending on culture compatibility, team and manager chemistry, skill matching, and so on. Receiving an offer—or not—is not at all representative of your personal or professional worth.

(Not) Getting an offer

Firms offering internships in sufficiently large numbers (say, more than 50 interns per year) have an unwritten commitment with the career offices of feeder schools: they need to make offers to at least 50% of candidates. Otherwise, the prospect is too risky for applicants, and schools will not supply first-tier candidates. Fifty percent isn’t bad.

Top buy-side prop trading firms and hedge funds do not compete much on compensation. At the top end, an alpha researcher will receive a $450–500K package, inclusive of sign-on, salary, and guaranteed bonus. Software engineers, execution research, risk, and data roles typically fall somewhere between $250K and $400K. Part of the compensation differential reflects skill, and part compensates for risk and terms of employment.

Regarding risk: signal researchers are expected to produce signals. If they do not, in the long run either they are let go, or the firm flounders and dies. Regarding terms of employment: signal researchers may face longer non-compete periods (between one and two years). These non-compete periods are especially costly for researchers, both because of foregone income and because their competitive advantage erodes over time.

Some general advice:

• You have little bargaining power and your contract will be fairly standard, so there is usually no point in spending $10K on a lawyer to review it. If you have multiple offers, compare the terms, look for glaring differences, and make up your mind.

• Your first job is the most consequential in shaping your future career path. In order of importance, learn about:

  • Job description: do you understand it? Is it what you want?

  • Team: do you like your future manager? Will they be a good mentor?

  • Environment and culture: do you like the firm’s atmosphere? If you are unsure, ask to chat informally with a few future colleagues—they will say yes.

  • Fit: do you have the right personality traits for the job?

Three more heuristics:

• Always think about the next job and the job after that. Does this role prepare you for your longer-term goals? Your next job should be a mix of tasks you already know how to perform and tasks you do not yet know how to perform but want to learn. Being greedy and thinking only one step ahead can trap you in a local maximum. Thinking more than two steps ahead seems too complicated and relies on too many unknowns. Two steps ahead feels about right.

• Compensation is very important—you are not applying to be the future Mother Teresa. But when thinking about compensation, consider its growth rate and the likely duration of your career. Researchers are a bit like athletes: they experience slow time decay, but their main enemy is on-the-job injury. For a quant researcher, burnout or loss of motivation is a greater threat to long-term compensation than starting pay. In other words, first-job compensation is a constraint to satisfy, not an objective to maximize. Often, the highest-paid job is not the most interesting or the most useful in the long run.

• Non–alpha-related jobs can be extremely intellectually satisfying. Work on data, execution cost measurement, optimization, or risk: these are all deep subjects, and you can have a long and rewarding career in any of them. The road to hell is paved with mediocre alpha researchers who fail to achieve their goals and burn out in their early 30s. A life of purpose may not be the first thing that comes to mind when working in finance, but to the extent that it is in your power, pursue it.

About 30–50% of you will not receive a return offer and will have a bad week. What is the recourse? Obviously, apply elsewhere. And what is the realistic outcome? Probably not landing your first or second choice. You may find roles at smaller buy-side firms, start on the sell side, or even work at fintech vendors. This may feel devastating to straight-A students who have never experienced a real setback, but it is not. First, learning to endure failure and suffering is a valuable life skill, and it is easier to learn early. Second, a disproportionate number of very successful quants did not start in top-tier firms or in their dream jobs. You are in very good company.

A Life as a Quant in Finance

Now you have a well-paying job that pleases your parents. What next? This is the most personal section and is based on a sample of one rather ordinary employee, so take it with a grain of salt. You might instead ask truly successful employees—the founders and hedge fund owners—but do so very carefully, since some of them are the love children of a schnauzer and Vlad the Impaler.

As a pet project, over the years I have asked many (many = 50–100) successful traders, algo developers, and portfolio managers what makes a great analyst on their team. The answers have been remarkably consistent.

• Curiosity. People who read articles and scientific papers on their own, maybe during weekends, for the sheer pleasure of finding things out.

• Creativity. Like obscenity, hard to define but easy to recognize when you see it. Roughly: looking at the same thing everyone else looks at, noticing something different, and proposing an original course of action. Most ideas do not survive scrutiny, but a few are brilliant.

• Humility. When something does not work, admit it early and openly, examine why, and move on. In practice, humility (as described to me) is both a willingness to take responsibility and an openness to experience.

• Integrity. Following both the letter and the spirit of the rules—the team’s, the firm’s, and the regulators’.

A couple of personal comments on this list. First, these qualities are highly correlated, and even their definitions overlap. There is a single trait that probably explains 85% of their occurrence. I cannot say whether this trait is innate or cultural, but I am fairly confident that by the time you join a firm as a researcher of some kind, you either have it or you do not.

Also of interest: not a single person mentioned “capability,” “mental throughput,” or “puzzle-solving” as a defining quality—yet we select in part on the ability to solve puzzles. Go figure. In fact, many of the people I interviewed said that almost everyone can learn to perform a given task competently or work hard to execute instructions.

Equally notable, not a single person mentioned soft skills such as empathy or communication. Indeed, some of the very best investors I know—while being good people deep inside—have the social skills of a thermonuclear reactor. Finally, every manager I interviewed sees their employees as researchers, not as soldiers or mere doers of tasks.

Assuming you do have these qualities, you may want to add some structure to your life as a researcher in order to be more effective.

1. Let’s start with the masters. Read the following three essays. They are short and full of useful advice.

   1. You and your research by R. Hamming. This is the most practical of my recommended reading. Please read this over and over again. Favorite sentence: “I started asking, ‘What are the important problems of your field?’ And after a week or so, ‘What important problems are you working on?’ And after some more time I came in one day and said, ‘If what you are doing is not important, and if you don’t think it is going to lead to something important, why are you at Bell Labs working on it?’ “

   2. Real-life mathematics by B. Beauzamy. By a mathematician doing actually applied mathematics. Favorite sentence: “Real-life mathematics [does] not require distinguished mathematicians. On the contrary, it requires barbarians: people willing to fight, to conquer, to build, to understand, with no predetermined idea about which tool should be used.“

   3. Ten lessons I wish I had been taught by G.C. Rota. Although this is a bit more academic, it is extremely useful. For example, the first item is on “lecturing”, but it’s really about communicating ideas effectively. Favorite lesson (from Feynman, actually): “You have to keep a dozen of your favorite problems constantly present in your mind, although by and large they will lay in a dormant state. Every time you hear or read a new trick or a new result, test it against each of your twelve problems to see whether it helps.“

   4. Cargo Cult Science by R. Feynman. This is the commencement speech he gave in 1974 to Caltech. Many prescriptions seem to have been written with the quantitative researcher in mind: “In summary, the idea is to try to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgment in one particular direction or another […] The first principle is that you must not fool yourself--and you are the easiest person to fool. So you have to be very careful about that. After you’ve not fooled yourself, it’s easy not to fool other scientists. You just have to be honest in a conventional way after that.”

   5. Career Advice, by T. Tao. More of meta-advice (which points to, for example, another Hamming essay). Focus on advice for “Graduate” and “Postdoctoral” levels.

2. You can be successful (especially as an alpha researcher) in one of two ways. The first: you identify a completely new opportunity. Example: Gerry Bamberger at Morgan Stanley in the 1980s developing statistical arbitrage. Also in the 1980s: early index rebalancing strategies and convertible arbitrage. The second: you apprentice on a team that already has a successful strategy, learn the trade, and improve it marginally. Unsurprisingly, the overwhelming majority of successful traders belong to the second class. The lesson is simple: try to join a team and a firm that have a habit of being successful. Do not think you can make a huge difference right away, and do not fall for the poetry of the underdog.

3. Relatedly, since you are an apprentice, get one or two mentors—people who are good role models because of their career paths and how they got there. Learn from them, ask for advice, keep in touch, and thank them. Years later, if they need your help (it does happen), offer it before they have to ask.

4. Develop your research agenda.

   1. Write down your open questions as soon as they arise, and organize them in a document. You may even have a research agenda that is not strictly related to what you do at work.

   2. Based on your questions, search for papers on arXiv and SSRN (organize RSS feeds), scan literature digests—past and present—provided by sell-side research organizations, and follow a few targeted academic journals.

   3. Read at least one paper a week. Start with the introduction, skip the literature review, go straight to the result statements, and, if they are interesting, read the proofs and experiments.

   4. If you find an old paper useful, search on Google Scholar for the most cited papers that cite it.

   5. Every month, ask yourself what you learned over the previous month, quarter, and semester, both on the job and in your broader research efforts. Write it down. You can use a tool like Anki to memorize the main points.

   6. Collaborate and exchange ideas. Do not be paranoid. No one is going to steal your idea. The real risk is that they will not even listen to you.

5. Talk to and learn from people with skills complementary to yours.

6. Finance researchers are, understandably, very conservative. Failure is risky, and there is downside protection in following established recipes. As much as is reasonable, question everything. If something still does not make sense after repeated questioning, you have found a research opportunity. One reason why 90%+ of traders and portfolio managers are only marginally successful (in addition to having had poor mentors—see above) is that they stopped asking uncomfortable questions.

7. Develop early a map of the relevant existing data sets in your area. Because you will be joining an established group, this should not be too hard. Then, to make progress, ask yourself:

   1. What data would be nice to have?

   2. Which entity has the data, or a usable proxy? Maybe they do not sell it, but they could?

   3. What would you do if you had this data? Would your approach fundamentally change, or would you simply scale the existing one?

8. Make the data science department your best friend.

A final, non–strictly professional piece of advice: you will spend more time working with your colleagues than with your partner, spouse, or family. If you have to suffer at work, try to suffer successfully—by sharing a strong common purpose with your colleagues and pursuing it in the best possible way. The accumulated wealth from having worked at several firms will not come from your W-2s, but from the relationships and friendships you build along the way.