If you ask most students what “quant finance” looks like, the mental image is pretty dramatic: solitary math geniuses, superhuman coding skills, constant pressure, and strategies that somehow never fail. It’s intense, prestigious, and, let’s be honest, intimidating. Admit it, you had a similar idea.
Some of that reputation isn’t entirely wrong. But the reality, at least the one described by Sumit Sourabh, the speaker at our recent event, turned out to be far more grounded, human, and reassuring. At the event, Sumit, a director and quant trader at ING, shared his path into the field, clarified myths, and gave advice that felt refreshingly useful. That was one of the best parts of the evening: it was accessible, open, and unusually honest about what the work really feels like, and whether it’s the right fit for you. Why his perspective resonated for some, and not for others, becomes clearer once you understand who he is.
Sumit Sourabh is a Director and xVA Quant trader at ING, where he has spent over nine years working on quantitative modelling for pricing and risk. However, his academic journey did not begin in finance. He completed an integrated master’s degree in mathematics and computer science in India, followed by a PhD in mathematics at the University of Amsterdam.
That already breaks a major myth: quant finance is only for those with a background in finance. As Sumit put it, if you bring strong technical skills from mathematics, statistics, and programming, finance is something you can learn along the way. The field is built for that kind of transition.
Quant finance is best understood as a field within finance that applies advanced mathematical and computational methods to address complex financial problems, such as risk management and the pricing of derivatives. Rather than a single job or technique, it functions as a methodological layer - a way of approaching financial questions by turning them into models that can be analysed and implemented. The questions are broad, but concrete. How might interest rates evolve? How would the price of an option change if markets shift? How do you measure and hedge risk so that losses remain manageable?
All areas of finance deal with unpredictable, moving prices. Quant finance differs in how those movements are handled. Rather than relying only on summary measures or predefined frameworks, it builds explicit models of how prices and risks evolve through time.
In this setting, uncertainty is managed by, and to a feasible extent, reduced by models - mathematical descriptions of possible future behavior, tested against data. This mindset matters. Throughout Sumit’s talk, quant finance was consistently presented as a structured process similar to engineering: state assumptions, build a model, test it, then implement it in a software.
That’s why people from many backgrounds thrive here. For STEM students, the thinking feels familiar. For those with financial expertise, the technical methodologies can be learned. As Sumit noted, most of his team didn’t come from finance.
When asked why quantitative models became so valuable, Sumit’s answer was simple: finance matured. Markets used to be slow and managed mostly by humans. Trading happened by phone, financial products were simpler, and markets were less automated and more opaque.
Then, two things evolved in parallel. Financial instruments became more complex - derivatives such as options and futures gained popularity, and strategies could no longer be priced by hand. Technology advanced to the point where markets now run at machine speed. Put those together, and intuition stopped being enough.
As Sumit summed it up: “It’s a combination of smarter, more efficient portfolio management combined with more exotic trading.”
Quant finance isn’t one job. It spans different environments, using similar tools in distinct ways. Sumit outlined four main areas:
Banks
Large banks like ING, Goldman Sachs, or JPMorgan sit between clients and markets. Clients seek stability and protection, while markets are volatile and uncertain. The bank connects the two.
Quants at banks build models to price contracts, measure risk and ensure exposures remain within strict limits. Their work is responsibility-heavy, shaped by regulation and long-term commitments, and focused as much on the bank’s own stability as on the client’s.
Within banks, quants usually fall into two broad groups:
Similar technical skills. Very different tempo and environments.
Hedge Funds
Here, firms like Citadel and Brevan Howard trade directly on the behalf of investors. The aim is simple: generate returns while controlling risk. Quants build models that decide what to trade and when. Results come fast, and performance is the final judge.
Market Makers and Proprietary Trading Firms
Well-known firms in this space include Jane Street or Optiver, who provide liquidity to financial markets by continuously offering buy and sell prices. This allows others to trade immediately, without waiting for a counterparty. Quants build systems that adjust these prices in real time and manage many small risks at high speed.
Asset Managers
Asset managers invest for the long term, often for pension funds. Quants help design portfolios that survive market ups and downs. The pace is slower, but decisions compound over years.
Understanding quant finance conceptually is one thing. Working in the field is another. So what does this actually look like in practice? One of Sumit’s key points was how different quant work feels from academia. In research, code explores ideas. In industry, it becomes infrastructure that must run every day, at scale, on time.
Much of the work happens inside large, existing codebases. You’re not just building models, you’re integrating them carefully, making sure nothing slows down or breaks overnight. Markets open whether you’re ready or not.
This is where roles diverge. In banks, front-office quants, the type of role Sumit works in, move fast and live with real deadlines. As Sumit noted, often, “You have an agreement with the desk that numbers should be ready at eight in the morning. If they’re not, you have a big problem.”
Model validation roles move slower, focusing on assumptions and conceptual soundness. For those who prefer depth over speed, that environment can be a better fit.
Outside banks, the balance shifts again. Hedge funds and market makers “are more adventurous” as Sumit noted, they offer more freedom and more pressure as the speed is enormous. Asset management, by contrast, trades speed for longer horizons.
The takeaway is simple: quant finance isn’t one lifestyle. Knowing how the work feels matters more than the title.
Sumit’s hiring philosophy boils down to three things: technical depth, communication skills, and genuine interest.
“I don’t care if you’ve never heard of options,” he stared “I do care if you can’t program properly.”
Programming is non-negotiable, especially in front-office roles. But it’s not about memorizing tools. It’s about understanding what your models mean, and when not to trust them. Shallow knowledge gets exposed quickly.
On the other hand, though often underestimated, soft skills are equally as important. Quant work is deeply collaborative. You work with traders, validators, risk managers, and engineers, all of whom speak different professional vocabularies at different levels of depth.
Being technically brilliant but socially unaware is a liability. The skill isn’t explaining everything you know. The real skill is sensing what the person in front of you actually needs.
“If you explain your algorithms in too much detail,” Sumit said, “people will stop listening.”
Initiative matters too. You’re expected to notice problems and take ownership, a shift that’s often harder than learning any programming language.
Feeling a little nervous? Every headline screams that AI is coming for your job, especially in finance. “At ING, Copilot is something that everyone uses, pretty much,” said Sumit. But don’t panic: adoption in banks is careful and regulated, particularly for pricing models. For roles like quant finance, Sumit believes AI is not a danger: it’s an assistant, not a replacement.
At this point, a natural question arises: if you’re still at university, what can you realistically do to enter the industry? Sumit’s advice was direct. On grades: “They might help you in your first job, that’s it.”
He recommended side projects, competitions, reading the Financial Times, and talking to recruiters before you feel “ready.” University teaches you how to think, but not how the work actually looks. Trying to solve real problems matters.
Looking back, though, the most memorable moment wasn’t about programming languages or interviews, it was about position.
“You don’t want to be the smartest person in the room. You want to be in an environment where you are the least smart person, so that you learn from others.”
It’s a useful statement to sit with.
Do you want a job where you feel competent quickly, outshining others, or one where you’re regularly stretched by smarter people and harder problems? Quant finance tends to be the second.
And if that sounds interesting rather than terrifying (though a little intimidation is acceptable), that’s probably the best signal of all.
