Quantitative Developer

A quantitative developer ("quant dev") writes and maintains the software that turns mathematical trading and pricing models into working systems - things like pricing libraries, risk calculators, backtesting tools and the code that sends orders to exchanges. They sit between quantitative researchers (who design the models) and traders or risk managers (who use them), and they work mainly at hedge funds, proprietary trading firms, investment banks, asset managers and the technology vendors that sell systems to those firms.

Starting salary: £40k–£65k
Live graduate jobs: 17 open now
Main way in: Graduate schemes at investment banks, usually under a…
They usually ask for: A degree in computer science, maths, physics, engineering…

Approximate graduate salary

Typically around £40,000–£65,000 to start, though this varies very widely. Bank and vendor graduate roles usually sit at the lower end of that; hedge funds and proprietary trading firms can start considerably higher, often with a meaningful bonus on top. Location matters too - London pay is well above regional technology centres such as Edinburgh, Leeds or Belfast, where quantitative technology teams also exist.

What you'd actually do

Writing and debugging code - most commonly Python, C++, Java, C# or Rust - that implements a pricing model, a signal calculation or a piece of trading infrastructure, then testing it against known results or historical data.
Taking a model a researcher has prototyped in a notebook or spreadsheet and rebuilding it as production code that runs reliably, fast and repeatedly without a human babysitting it.
Building and maintaining data pipelines: pulling in market data (prices, trades, order book snapshots), cleaning it, handling missing or wrong values, and storing it so research code can query it quickly.
Profiling and optimising code that is too slow - working out whether the bottleneck is the algorithm, memory access, network latency or a database query, and fixing it.

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Sitting with traders, researchers or risk staff to work out what a system actually needs to do, and translating vague requests ('can we see our exposure if rates move') into a concrete specification.
Investigating production problems - a report that didn't generate, a number that looks wrong, a feed that stopped - sometimes under time pressure because the trading day is running.
Code review, writing tests, and the ordinary engineering housekeeping: version control, build pipelines, deployment, documentation.

How graduates get in

Graduate schemes at investment banks, usually under a technology or markets-technology heading rather than being advertised as 'quant developer'. These are the most structured route and typically recruit a year ahead with summer internships feeding into them.
Direct graduate hiring at hedge funds and proprietary trading firms. These are usually standalone roles rather than rotational schemes, with small intakes and a heavy focus on coding and problem-solving tests rather than competency interviews.
Converting from a general software engineering job. Very common: people join a bank, fintech or technology firm as a developer, get exposed to pricing, risk or market data work, and move across. This is a realistic route if you miss the graduate milkround.
A master's or PhD in a quantitative subject (maths, physics, statistics, computer science, financial engineering) used as a route in. Common at firms doing heavier modelling work, though for a pure development role strong coding usually matters more than the research credential.

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Financial technology vendors and consultancies - firms that build pricing, risk or trading platforms sold to banks. Often an easier first step, and the domain knowledge transfers well if you later move to a bank or fund.
Unusual but real: moving in from an exchange, a market data provider, or a regulator's technology function, where you pick up the same data and systems knowledge.

What employers ask for

A degree in computer science, maths, physics, engineering, statistics or a similarly numerate subject. The subject genuinely does matter here, more than in most graduate jobs - you need to be comfortable with both code and maths.
Strong programming ability that you can demonstrate under test conditions. Most employers will set a timed coding exercise, a take-home project, or a live pairing session; this carries more weight than your CV at many firms.
Grades: a 2:1 is the usual floor and many quantitative trading firms effectively look for a first plus strong A-levels in maths. Some of the smaller technology-led shops care far less about classification if you perform on the technical tests - this varies a lot.
Postgraduate study is common but not required. A master's or PhD helps for roles closer to research; it is not needed for infrastructure-heavy development work.

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No mandatory professional qualification. The CFA (Chartered Financial Analyst) and FRM (Financial Risk Manager) exist and some people take them, but they are finance qualifications rather than developer ones and are rarely expected. Some bank roles require FCA registration if you support regulated activities.
Evidence of building things outside coursework - personal projects, open-source contributions, competitive programming, a backtesting engine you wrote yourself - is valued heavily, particularly by funds and trading firms.

Skills that matter

Programming in a production language (C++, Java, C# or Rust) alongside Python

Python is the usual language for research and analysis, but the systems that must be fast or run continuously are generally written in a compiled language, and you are often moving code between the two.

Numerical and statistical literacy

You need to understand what a model is doing well enough to spot when an output is wrong, and to know why floating-point arithmetic, interpolation choices or random number seeding can silently change results.

Performance and systems thinking

A lot of the job is making things faster or more reliable, which means understanding memory, concurrency, caching, databases and networks rather than just writing correct logic.

Data handling and scepticism about data

Market data is messy - gaps, bad ticks, corporate actions, time zone and timestamp problems - and a model fed bad data produces confident nonsense.

Translating between technical and trading language

Your users are traders, researchers and risk staff who describe problems in their own terms, and getting the requirement wrong wastes weeks of build time.

Working carefully under live pressure

Mistakes in code that touches pricing or order flow have immediate financial consequences, so testing discipline and the ability to debug calmly while markets are open both matter.

Putting these on a CV? We counted which skills 12,914 live graduate adverts ask for, and what to do with the answer — part of our application advice.

Where it leads

First one to three years: working on well-defined pieces of a larger system under supervision, learning the firm's codebase, the products it trades and the data it uses. At banks this may include rotations across desks or technology teams.
Mid-level: owning a system or a component end to end - you are the person who gets called about it, and you design new features rather than implementing someone else's design. Timelines here vary widely by firm size; small funds promote on capability rather than tenure.
Senior or lead developer: architecting platforms, setting technical direction, reviewing others' work and mentoring juniors. Many people stop here deliberately because it stays hands-on and is well paid.
Branching points: some move sideways into quantitative research or trading (easier at funds than banks, and usually requires demonstrated modelling ability); some move into engineering management or head-of-technology roles; some move to a technology vendor or fintech where the same skills apply to building products.
Longer term, common destinations include CTO or head of engineering at a smaller fund or fintech, independent contracting (well established in London banking technology), or a move into general software engineering outside finance, which the skills transfer to readily.

What people get wrong

“It's basically the same job as a 'quant' - you spend your time inventing trading models.”

Quantitative research and quantitative development are usually separate roles. Quant devs mostly build, optimise and maintain the systems that models run on. Some firms blur the two and expect you to do both, but if you want to design models rather than engineer them, check what the specific role involves before applying.

“You need a PhD and deep finance knowledge to get hired.”

For development-focused roles, demonstrable coding skill is usually the binding constraint. Plenty of quant devs come in with a bachelor's in computer science and no finance background, and learn the products on the job. A PhD matters far more for research seats.

“Everyone is at a hedge fund doing high-frequency trading, and the hours are brutal.”

High-frequency and low-latency work is a small, specialised corner. Far more quant devs work on overnight risk systems, pricing libraries, regulatory calculations or portfolio tools at banks and asset managers. Hours in those settings are often closer to normal technology jobs than to investment banking deal teams, though month-end, go-live and market stress periods are heavy.

“Once you're in, you're locked into finance for life.”

The core skills - performance-critical code, large data pipelines, numerical computing - are portable, and people move out to technology firms, energy trading, insurance and scientific computing. The finance-specific knowledge is the part that doesn't travel, not the engineering.

Where this varies

The job differs a lot by employer type. At a large investment bank you are likely to work in a big team on a long-lived codebase, with formal release processes, change control and regulatory constraints, and the work may be as much about risk reporting and regulatory calculation as about trading. At a hedge fund or proprietary trading firm, teams are small, you may own a system alone, the feedback loop with traders is immediate, and the engineering standards can be either exceptional or improvised depending on the firm. At a technology vendor or fintech you build a product for many clients rather than one trading desk, which means more generality and more client-facing work. Asset managers sit somewhere in between, with more emphasis on portfolio construction, performance attribution and long-horizon data than on latency. Job titles are also inconsistent across the market: the same work might be advertised as quantitative developer, quant engineer, trading systems developer, risk technologist or simply software engineer, so read the description rather than the title.

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General guidance about the role across the UK market, not about any specific employer. Entry routes and requirements vary — always check the individual job advert.