What do quants do – and how do you become one?

• Quant finance jobs combine mathematical and engineering skills
• Quants in finance look for mathematical relationships between underlying assets, or create derivatives based on those assets
• Quants in finance also (increasingly) work in areas like risk
• You make the most money in quant finance when you’re closely associated with the profit and loss made by traders or portfolio managers

What is a quant and what do quantitative finance jobs involve?

To the uninitiated, quants in finance fit the stereotype perpetuated in The Big Short: number-crunching mathematics wizards working for traders who aren't quantitative at all. But that was 2008. Times have changed.

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Quant jobs used to be about derivative pricing models. But in 2026, quantitative finance is a catch-all term that covers numerous jobs. There are quant traders. There are quant developers and quant researchers. These often used to be a double act; researchers came up with mathematical trading models, and developers coded the systems that implement those models. AI is blurring the two.

You won’t just find quants at banks and hedge funds. Quantitative ‘prop trading’ firms has exploded. The likes of Jane Street, Hudson River Trading and Citadel Securities have become mainstream names. Jane Street famously made a record-breaking $39.6bn in trading revenue last year, and has broken the record for quarterly revenue hauls multiple times. These firms operate a number of strategies, but they're most well known for electronic market making; rather than betting on the price of an asset going up or down, they'll execute a massive number of trades on a daily basis, quoting buyers and sellers different prices then pocketing the 'spread'. These firms operate high-frequency trading (HFT) strategies where an asset is sold in a matter of milliseconds, and increasingly use mid-frequency market making strategies where positions are held from minutes to days.

Different types of organisation need quants to do different things. Banks, for example, employ a lot of quants to write easily explainable models to calculating the changing value of assets included in their trading books. Financial services regulators demand that these models can be easily understood. High frequency trading (HFT) firms, by comparison, hire an elite niche of quant developers who can fine tune trading systems to make them faster by a matter of picoseconds.

In banking, there are also quant strats, an even more ambiguous niche. Strats roles vary from bank to bank, but they’re often commercially-savvy quantitative generalists assisting a trading desk. Quants exist beyond the trading floor as well, most prominently in risk management. Risk roles are far less flashy, but are generally seen as more sustainable careers. Quants also help in areas like HR to help analyse patterns in the workforce. 

What does a quant actually do?

Quant traders, put simply, analyse data from a variety of sources and use it to create a trading strategy for a particular asset, or group of assets. There are various strategies, including statistical arbitrage, machine learning and mean reversion (which we explain here). In prop trading firms or quant hedge funds, the title is often interchangeable with a quant researcher, whereas in more traditional hedge funds it's more likely that you'll be a systematic portfolio manager. Quant researchers who aren't actively trading will make similar models to quant traders, but use them to inform the investment decisions of others. 

Both these quants will spend their day monitoring their models and keeping abreast of the news, making tweaks when necessary. As they do this, they’ll work on larger-scale improvements to models in the background, or develop new ones. Researchers and traders alike are searching for strategies with ‘alpha’ (better profits compared to the market).

Giuseppe Paleologo, head of quantitative research for Balyasny, said in a 2024 quant careers guide that the role is essentially an apprenticeship. He said the majority of successful quants join an established, successful team, learn from them, and make marginal improvements on their work. 

Ruizhou Ding, a Citadel Securities quant, told us he’ll work on model improvements throughout the day, then apply them 90 minutes before clocking off. He said he also works on overnight experiments to influence how he tweaks models the following day. Examples of these experiments include executing trades at different times of the day to see how it affects risk.

Quant developers, meanwhile, are polyglot programmers that turn a researcher's idea into reality. They’ll need data science fundamentals to understand model code produced by researchers, and low-level programming experience, most often in C++, to interact with exchanges and put those models into production.

Quantitative risk professionals in banks tend to be even more maintenance focused, since models are mature and fewer risky trades are made compared to hedge funds or HFTs. 

Paleologo worked in risk for Citadel, Millennium and Hudson River Trading. He said on Bloomberg’s Odd Lots podcast that quant risk involves finding “hidden bets” where separate strategies overlap and affect each other. 

Risk teams can also decide how a fund allocates capital. In a pod-based hedge fund, Paleologo said you’re essentially managing a portfolio of pods as though they were equities. At some funds like Verition, risk quants have the ability to influence hiring strategy of trading teams as well as capital allocation.

Quantitative Strategists are a little different; their role differs from bank to bank, but they’re akin to a dedicated product manager for their trading desk. Strats liaise with both tech and traders to prototype and implement quantitative tools. Usually, they tend to quickly deliver prototypes, free from the constraints of legacy infrastructure, with successful products then being built out by engineering teams. 

It would be prudent to try and talk to current or former strats at whichever bank you want to join to understand the specifics of its strat culture. You can find a lot of them at hedge funds.

What are hours like as a quant?

100 hour weeks aren’t much of a thing in quant finance. In our 2025 salary and bonus report, quant finance staff reported working 47 hours per week, making it the seventh best sector for working hours, while risk staff worked 50.6 hours. 

There are intrinsic risks to the role, however. Two weeks after the death of Bank of America banking analyst Leo Lukenas, an algorithmic trader at BofA, Adnan Deumic, died of suspected heart complications. Deumic worked ~60 hour weeks, according to the New York Post, in shifts of 10-12 hours. Those periods were very intense, and he reportedly wasn't able to take a break long enough to get a cup of coffee.

Quants in electronic trading handle similar intensity. At Jane Street, for example, traders max out their monitor space with tools that can be as small as six pixels tall.

For hedge funds, meanwhile, Paleologo said “you will spend more time working with your colleagues than with your partner, spouse or family.” He suggests, however, that the real challenge of the role is the intensity rather than the hours, and notes that “the road to hell is paved with mediocre alpha researchers who did not achieve their goals and burned out by their early 30s.” Quant risk, he alleges, can be more conducive to a “long and great career” if you’re one such researcher.

What is your career path as a quant?

In a bank, your career path is the same as anyone else: analyst to MD… much to the chagrin of quants themselves. Your promotions there are less to do with your quality of work, and more to do with your time served. Performance does speed things up, but you won’t find a 24-year-old quant research whiz promoted to MD any time soon. This is also because senior people in banks are expected to manage teams; using tech as a similar barometer, the furthest you can make it as an individual contributor is usually VP.

Banking quants may therefore want to seek exit opportunities to hedge funds and trading firms. There, careers are a bit less linear and more performance-based. The ultimate goal, if money is your aim, is presumably to become a portfolio manager, where much of our advice on sales and trading jobs applies. There are other management opportunities on the buy-side, however, and if you don’t want to be a PM, many hedge funds have director roles for quant research, risk and more.

If you don’t intend on working in a bank beforehand, you’ll need to get an internship at a top firm. These are not easy to obtain by any stretch of the imagination. Citadel and Citadel Securities accept just 0.4% of their applicants. Balyasny’s acceptance rate is 0.5% while Hudson River Trading's acceptance rate for engineers is 0.1%. Even outside of the marquee programs, recruiters have told us that just one in 100 candidates will receive an offer. 

Depending on your technical expertise, you might be able to come in after a stint in Big Tech. If you work in machine learning or on hardware, you’re in a comparatively niche candidate market. AI labs and quant firms are also battling it out for talent right now, so moving from one to the other is a feasible career path.

Beware, however, the prevalence of non-competes, which can derail your career if not handled correctly. Trading firms don’t want the alpha you generated to be implemented by another firm the moment you leave, so they will demand that you spend a period out of the market when moving from one firm to the next. These non-competes can reach as much as three years, if you’re senior. 

The ‘gardening leave,’ as it's called, is at least paid, and you can still work for firms deemed not-competitors, giving you licence to work at flashy employers outside of finance like OpenAI, depending on the terms of your non-compete. The severity of these depends on both your firm and your location; funds in the US have lobbied for non-competes of up to four years whereas the UK wants to ban them outright. 

Which skills will you need for a career in quant finance? 

Mathematics and computer science fundamentals are essential. You’ll need to know probability, statistics, stochastic calculus and more. Questions on machine learning are becoming increasingly common in job interviews, as are questions on linear regression.

You’ll also need elite problem-solving skills. Quant finance interviews often involve complicated or bizarre brainteasers to test your critical thinking. You can see some of those questions here.

As mentioned, quant developers will usually need experience in C++. You’d also be best served to sharpen your Python skills, as the language can be used for both data science and machine learning. Top Python libraries include Pandas for data analysis and PyTorch for ML modelling. Some firms, like Jane Street, use lesser-known languages like OCaml, although knowing these is not always a pre-requisite if you have experience in a similar, more popular language.

As for soft skills, Paleologo said in his career guide that quants should be creative, curious, humble and possess integrity.

Other skills are dependent on your area of expertise. If you’re a hardware engineer, you’ll need to be familiar with FPGAs, ASICs or GPUs and their accompanying programming languages like Verilog or CUDA. 

Skills also depend on the asset class you’re trading in. Algorithmic crypto traders, for example, are more likely to use Rust than someone trading equities.

Which qualifications will you need for a career in quant finance? 

The vast majority of professionals in quant finance are STEM graduates in one way or another. When we analyzed a section of Jane Street’s 2025 intern class, we found that more than two thirds of interns studied computer science in some form, while just over a third studied mathematics. At Citadel Securities and Citadel, roughly 80% of interns studied at least one of the two subjects.

Then there’s the question: to PhD or not to PhD. This depends more on the type of firm you want to join. Banks and hedge funds are both thought to appreciate PhD graduates who don’t need to be brought up to speed as much, whereas newer trading firms prefer more impressionable undergraduates that they can mould in their image.

Recent years have also seen a rise in prominence for masters in financial engineering (MFE) courses. These are specialist degrees focused on applying math and computer science to finance, but are a double-edged sword. Top courses, like those on offer at Baruch or Princeton, see their graduates earn starting salaries of over $200k. MFEs are less popular in Europe, but an alternative is the DEA (French for a Masters in Finance). These are offered by the Grandes Écoles but its thought that graduates from these schools prefer Baruch's MFE instead. 

The key appeal of joining an MFE is its focus on securing internship and graduate placements through strong recruiting pipelines. MFEs at less prestigious institutions, therefore, can be a waste of money; a survey of MFE graduates by Paleologo last May revealed that 30% of them did not have jobs. Proceed with caution.

What are salaries and bonuses like in quant finance?

Revenue generating quants (those whose strategies impact trading decisions) can earn exceptional amounts at any seniority. Using Jane Street as an example, partners in its UK entity earned £14m on average in 2024, while graduates are hired on packages of up to $500k. 

Not everywhere is Jane Street, of course. Paleologo said that top buy-side alpha researchers (quants whose strategies regularly beat the market) usually earn up to $500k, while top quants in other fields usually earn up to $400k. 

Pay can vary wildly from year to year because bonuses make up a significant chunk of total pay. Our analysis of the H1B Visa salary database earlier this year suggested that the average salary for quant researchers in the US is a more modest $190k, while our compensation report revealed that employees in the quant finance sector earned $351k on average in 2025.

How is AI changing careers in quant finance?

Technology roles both in and out of finance are being transformed by AI. There's a lot of overlap between the skillsets needed for each job, but quant roles seem to be a bit better off, albeit with some changes to the nature of the role.

Petter Kolm, an ex-Goldman quant and director of NYU Courant's mathematics in finance masters, told us that AI is "changing the nature of the work that strong quants are expected to do." Tasks like routine coding, documentation and first-pass research can now be done much faster, so the most valuable candidates are those that "work at a higher level of abstraction." (i.e. candidates who can determine what the right problems that need to be solved are, and evaluate whether outputs are statistically and economically meaningful)

AI coding is a particular boon, but Jared Broad, CEO of open-source algo trading infrastructure firm QuantConnect, said that coding tools are "far from perfect and still require review and direction." He thinks that the tools will lead to increased productivity expectations rather than layoffs or reductions in the near future.

What roles are most at risk? Kolm said that junior quant researchers, quant developers and junior desk quants will have their roles changed, but not eliminated. They're likely to converge with other roles; for example, Kolm says that quant developers "may be expected to understand more of the modelling and economic content of what they're implementing."

The roles less susceptible are those that require more domain expertise and institutional knowledge. These include derivatives quants, senior desk quants and strats. Kolm said humans are needed there to explain complex topics to non-technical people, and so someone can be held accountable when things go wrong.

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