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Contents
What Matters Most
Why This Quantitative Developer Resume Works
How to Write a Quantitative Developer Resume That Gets Interviews
What to Include in a Quantitative Developer Resume
Quantitative Developer Resume Summary Examples
Quantitative Developer Work Experience Examples
Top Quantitative Developer Skills
Certifications for a Quantitative Developer
Common Quantitative Developer Resume Mistakes
Quantitative Developer Resume FAQs
William Tilson
Quantitative Developer
0016097721540
Summary
Quantitative developer with eight years building the systems behind trading and quantitative research at hedge funds and a bank in London. Bridges quants and engineering — turning models and strategies into fast, robust production code, building backtesting and execution infrastructure, and handling the data pipelines that feed it all. Built a backtesting engine that cut strategy research turnaround from days to minutes for the quant team. Works in C++ and Python on low-latency systems, market and time-series data, risk and pricing libraries, and the performance optimisation trading demands. Strong on both the mathematics and the software engineering, and meticulous about correctness where bugs cost real money. Calm under live-trading pressure. Looking for a quant-developer role with a fund, bank or trading firm doing serious systematic work.
Work Experience
Quantitative Developer
London Systematic Capital, London, UK
Apr 2017 – Present
- Turn the quant models and the strategies into fast, robust production trading code every day.
- Built a backtesting engine that cut the strategy research turnaround from days down to minutes.
- Build and maintain all the execution infrastructure and the low-latency systems in C++ and Python.
- Develop the data pipelines feeding the market, the reference and the time-series data to research.
- Maintain the pricing and risk libraries with rigorous testing where a bug costs real money.
- Optimise the performance and support all of the trading systems calmly under real live-trading conditions.
Quant Developer / Software Engineer
City Investment Bank, London, UK
Aug 2014 – Mar 2017
- Built pricing and risk tools for trading desks in C++ and Python.
- Developed the data feeds and the analytics supporting the quant research teams.
- Learned the market microstructure, backtesting and low-latency engineering on the job.
- Gained CQF and moved to a systematic fund as a quant developer.
Software Engineer
City Investment Bank, London, UK
Jun 2012 – Jul 2014
- Built backend systems and data tools as a software engineer in finance.
- Learned the C++, Python and high-performance engineering on real production systems.
- Built up the engineering depth that quant development is built on.
- Then earned the move into a full quant-developer role from there.
Education
MEng in Computer Science & Mathematics, Computer Science & Mathematics
University of Cambridge
Sep 2010 – Jun 2014
- Integrated master's combining computer science and mathematics, covering algorithms, numerical methods and software engineering. The blend maps exactly onto quant development. Built the dual foundation the role requires.
Certificate in Quantitative Finance (CQF), Quantitative Finance
CQF Institute
Jan 2016 – Dec 2016
- Certificate in Quantitative Finance covering derivatives, risk, and computational finance methods. It deepened the finance domain alongside engineering. Applied directly to pricing, risk and backtesting systems.
Highlights
Days to minutes
- Built a backtesting engine that cut strategy research turnaround from days to minutes for the quant team. Faster iteration lets researchers test far more ideas and find the ones that work.
Correct where it counts
- Holds rigorous testing standards on code where a single bug can cost real trading money. In systematic trading, correctness is not optional, it is the whole job.
Certifications
Certificate in Quantitative Finance
CQF Institute
Dec 2016 – Present
- Certificate in Quantitative Finance covering derivatives, risk, and computational finance methods. It deepened the finance domain alongside engineering. Applied directly to pricing, risk and backtesting systems.
Low-Latency C++ & Systems
CppCon Training
Apr 2019 – Present
- Certification in low-latency C++ and high-performance systems engineering. It supports the execution infrastructure and the fast, correct trading code built for the desk.
Key Projects
Low-Latency Execution System
Jan 2019 – Dec 2019
- Built core components of a low-latency execution system in C++, tuning the hot path so orders went out within tight time budgets under live market load.
Market-Data Pipeline
Mar 2020 – Oct 2020
- Designed the market and reference-data pipeline feeding research and live trading, making the feeds fast, clean and reproducible so models trained and ran on data they could trust.
Languages
- English (UK) — Native or Bilingual Proficiency
- Mandarin — Limited Working Proficiency
Professional Skills
- C++
- Python
- Backtesting Systems
- Low-Latency Systems
- Market & Time-Series Data
- Pricing & Risk Libraries
- Execution Infrastructure
- Performance Optimisation
- Numerical Methods
- Data Pipelines
Personal Skills
- Analytical Thinking
- Precision
- Composure
- Problem Solving
- Rigour
Activities & Interests
- News paper
- House Repair
- Smoking
- Horror Movies
- Shopping
What Matters Most
Before the detail, here is what actually decides a strong quantitative developer resume:
- Prove you bridge quants and engineering - show that you took a model or strategy and turned it into robust, tested production code, not just a notebook prototype.
- Name the systems you shipped: backtesting engines, execution infrastructure, pricing/risk libraries, market-data pipelines - the things a desk runs money on.
- Lead with C++ and Python depth and back it with a latency or throughput number; on a hot path, microseconds and nanoseconds are the currency reviewers read.
- Tie work to PnL, risk or research velocity - strategies tested per week, basis points saved on execution, a backtest cycle cut from days to minutes.
- Signal correctness discipline: testing standards, reproducible data, reconciliation - because a single bug on a live book costs real trading money.
- Place finance-domain proof (derivatives, microstructure, CQF) so it is clear you understand the models you are coding, not just the syntax.
Why This Quantitative Developer Resume Works
The sample is a mid-to-senior London quant developer with eight years across a bank and systematic funds. Here is what its choices get right for a recruiter screening for this role:
- The summary opens with the bridge identity - turning quant models and strategies into fast, robust production code - which is the single thing that separates a quant developer from a generic software engineer or a pure quant researcher.
- It anchors on one flagship system, the backtesting engine that cut research turnaround from days to minutes, so the reviewer gets a concrete, quantified outcome instead of a list of technologies.
- The C++/Python pairing is stated up front and reinforced by low-latency, execution and pricing/risk work, matching exactly what the screen is keyed to.
- The career arc is legible: software engineer into quant developer at a bank, then a move to a systematic fund - the standard path, signalling earned progression rather than a sideways jump.
- It foregrounds correctness 'where bugs cost real money' and composure under live-trading pressure, the temperament a desk lead actually worries about when hiring.
- Domain depth is placed where it belongs - an MEng in CS and Mathematics plus the CQF - so the math credibility supports the engineering rather than competing with it.
How to Write a Quantitative Developer Resume That Gets Interviews
A quant developer resume has to satisfy two readers at once - a hiring quant who wants to know you understand the models, and an engineering lead who wants to know your code survives production. These moves serve both:
Lead with the bridge, not a tech stack list
Open the summary by stating that you turn models, signals or strategies into production trading code. A reviewer can find C++ on a hundred resumes; what they screen for is whether you can take a researcher's idea and ship it correctly. Make that the first line, then let the stack follow.
Quantify latency, throughput and research velocity
Put numbers where they carry weight in this field: orders sent within a microsecond budget, a feed handler processing N million messages a second, a backtest cycle cut from days to minutes, tick storage compressed by a given factor. These are the metrics a desk actually tracks, and they read as insider proof.
Name the systems you owned
Trading firms hire by component. Spell out the things you built or maintained - backtesting framework, execution gateway, FIX/market connectivity, pricing library, risk engine, market-data pipeline - and state your scope on each. 'Built the core of the low-latency execution path in C++' beats 'worked on trading systems'.
Make correctness visible
Because a single bug on a live book loses money, treat testing and reproducibility as achievements, not housekeeping. Reference your standards - property-based tests on pricing code, a reconciliation harness, reproducible backtests pinned to clean data - so the reader trusts you near production money.
Show enough finance to prove you read the models
You do not need a PhD, but you must show you understand what you are coding. Reference the instruments and methods you have worked with - derivatives pricing, Monte Carlo, time-series and stochastic calculus, market microstructure - and credentials like the CQF that signal domain literacy alongside the engineering.
Keep it dense and technical, one or two pages
This audience rewards signal density. Cut soft generalities, keep bullets concrete, and let the technology, the numbers and the systems do the talking. One page early-career, two pages once you have shipped systems worth describing. Once the structure is clear, you can build it on a quant-ready template and drop your systems and numbers straight in.
What to Include in a Quantitative Developer Resume
Beyond the standard summary and experience, these sections carry disproportionate weight for a quant-developer screen:
A skills block split between languages/engineering (C++, Python, multithreading, Linux, build/CI) and quant domain (derivatives pricing, Monte Carlo, time-series, risk analytics) so both readers find their signal fast.
A projects or systems section - the single most useful add for this role - naming flagship builds like an execution gateway or market-data pipeline with the latency or scale figure attached.
Education that pairs a strong quantitative degree (CS, maths, physics, engineering) with any finance credential (CQF, FRM, a relevant master's); this is where math credibility is established.
A short stack line covering the real toolchain: KDB/q or SQL, QuantLib or an in-house pricing library, a backtesting framework, FIX, and your data stores.
Certifications that map to the work - CQF for domain, low-latency C++ or systems training for the engineering side.
Quantitative Developer Resume Summary Examples
These summaries cover different seniority and sub-industry angles around the sample, so you can match your own level - each is pronoun-free and ready to adapt:
Getting three lines to carry your bridge role, one flagship system and a latency figure without reading like filler is genuinely hard, and a flat summary sinks an otherwise strong desk application. For a senior or high-comp fund role where that opener has to land, you can have a specialist writer shape it with you.
Entry-level resume summary example
Quantitative developer with two years writing production code for a derivatives pricing desk after a master's in computational finance. Comfortable in C++ and Python, with hands-on work on a Monte Carlo pricing library, the unit and property-based tests around it, and the data jobs that fed it. Rebuilt a slow overnight risk batch into a multithreaded run that finished in under twenty minutes, freeing the desk's morning. Reads the maths behind the models - stochastic calculus, numerical methods, time-series - well enough to spot where a quant's notebook will break in production. Looking to deepen low-latency and execution work at a systematic fund or bank while staying close to the pricing and risk side of the business.
Senior-level resume summary example
Senior quantitative developer with eleven years building low-latency trading and research infrastructure at two systematic funds. Owns the execution stack end to end - a C++ order gateway holding sub-ten-microsecond tick-to-trade, FIX connectivity to a dozen venues, and the kdb+/q tick store behind both research and live trading. Cut median execution slippage by roughly fifteen percent by rewriting the hot path and the smart-order-routing logic. Leads a small pod of developers, sets the testing and release standards that keep a live book safe, and works directly with portfolio managers to take signals from backtest to capital. Strong across modern C++, performance engineering, market microstructure and the risk analytics that bound every strategy. Seeking a lead quant-developer role at a serious systematic shop.
Quantitative Developer Work Experience Examples
Different desks weight the role differently - execution, research infrastructure, pricing. These labeled sets show how to phrase quantified bullets for each, distinct from the sample:
Execution / low-latency developer
- Rebuilt the C++ order-gateway hot path to hold tick-to-trade under eight microseconds at the 99th percentile, lifting fill rates on the firm's two highest-volume futures strategies during peak liquidity windows.
- Implemented smart-order-routing logic across nine venues and a custom FIX engine, cutting median execution slippage by roughly fourteen percent and saving an estimated 2.3 basis points per parent order.
- Built a lock-free market-data feed handler in modern C++ processing 4 million messages a second, replacing a queue-based design that dropped ticks under bursts and corrupted the order book.
- Profiled and removed allocation on the critical path with custom memory pools and cache-aligned structures, shaving jitter so latency held within budget through the volatile open and close auctions.
Research / backtesting developer
- Designed a vectorised backtesting engine in Python and C++ that cut a full strategy research cycle from roughly six hours to under four minutes, letting the quant team test far more signal variants per week.
- Built a reproducible market-data pipeline over kdb+/q feeding both research and live trading, pinning every backtest to point-in-time clean data and eliminating the look-ahead bugs that had inflated prior Sharpe estimates.
- Wrote a transaction-cost and slippage model into the backtester so simulated PnL tracked live results within a few percent, ending the gap between paper performance and what the book actually earned.
- Parallelised the parameter-sweep harness across a compute cluster, taking an overnight 12-hour optimisation down to under forty minutes and unblocking same-day strategy iteration for three portfolio managers.
Pricing / risk library developer
- Maintained the desk's C++ derivatives pricing library covering vanilla and exotic options, adding a Monte Carlo engine with variance-reduction that converged on barrier products in a fraction of the prior runtime.
- Rebuilt the overnight risk batch as a multithreaded run that completed in under twenty minutes instead of three hours, delivering Greeks and VaR to traders before the European open every day.
- Introduced property-based and regression testing across the pricing stack, catching a discounting-curve bug before release that would have mispriced a multi-million-notional swaps book.
- Integrated QuantLib alongside the in-house library for benchmarking and validation, giving the quant team an independent reference check that surfaced two calibration discrepancies and tightened confidence in production prices.
Top Quantitative Developer Skills
List the engineering and the quant-domain skills side by side - a strong quant-developer resume proves depth in both, not just one:
Hard skills
- C++ (modern, low-latency)
- Python (NumPy / pandas)
- Derivatives pricing & risk models
- Monte Carlo & numerical methods
- Time-series & stochastic calculus
- Market & trade data pipelines
- kdb+/q or SQL
- Multithreading & performance optimisation
- Pricing libraries (QuantLib)
- Backtesting frameworks
- PnL & risk analytics
- Linux & build/CI
- FIX & market connectivity
- Market microstructure
- Execution & order-management systems
- Memory & cache optimisation
- Reproducible research data
- Statistics & probability
Soft skills:
- Analytical rigour
- Precision
- Composure under live-trading pressure
- Problem solving
- Communicating with quants and traders
- Ownership of production code
Extra tips
Report p99 or p99.9 tick-to-trade, never the average.
Mean latency hides the jitter that actually costs fills, so quoting the tail signals you profile the way a desk does.
Certifications for a Quantitative Developer
Quant development is skills- and degree-led, so none of these are required - but for a developer without a heavy-maths background, the right credential is a clean way to signal you read the models you code:
-
CQF
— CQF Institute Optional but the most relevant credential here - a part-time programme covering derivatives pricing, Monte Carlo, risk and machine learning; signals domain literacy for engineers moving into quant work.
-
CFA
— CFA Institute Optional; broad investment and markets grounding rather than a coding credential - useful for buy-side context, less so for pure low-latency roles.
-
FRM
— GARP Optional; strongest for developers on the risk-engine and VaR side who want to prove they understand the analytics they are building.
Common Quantitative Developer Resume Mistakes
These are the errors that get a quant-developer resume passed over by a desk lead:
- Listing technologies with no systems or numbers - 'C++, Python, Linux' tells a reviewer nothing without the latency figure, the message rate, or the backtest cycle you improved.
- Reading like a generic software engineer who happens to be in finance; if nothing in the resume shows you understand the models and the markets, you lose to candidates who do.
- Overclaiming the quant-research side - inventing alpha or strategy ownership you did not have invites questions you cannot answer, and desks check hard.
- Burying the flagship system in a wall of duties; the execution gateway or backtesting engine you built should be impossible to miss, not item five in a paragraph.
- Ignoring correctness and reproducibility - on code that touches a live book, no mention of testing or data discipline reads as a risk, not a neutral omission.
- Padding with soft adjectives instead of latency, PnL, risk and throughput numbers; this audience trusts signal density and discounts everything else.
Quantitative Developer Resume FAQs
The questions candidates most often ask when writing a quantitative developer resume:
A quant developer's resume centres on building production systems - execution, backtesting, pricing libraries, data pipelines - while a quant researcher's centres on designing and validating the models and strategies themselves. Frame yourself as the engineer who makes the research real, run fast and stay correct; lead with systems and code, not alpha discovery.
It must prove finance-domain fluency on top of strong engineering. A pure software engineer resume can stop at systems and scale; a quant-developer resume also shows you understand pricing, risk, market microstructure and the models you are coding, plus the correctness discipline a live trading book demands.
No PhD is required for most quant-developer roles - a strong master's or bachelor's in computer science, maths, physics or engineering is the common baseline, and depth of shipped systems matters more. A PhD is mostly expected for quant-research roles; for development, prove engineering strength plus enough quant literacy to read the models.
Show both, but weight C++ for low-latency, execution and pricing-library roles, and Python for research, backtesting and data work. Most desks want fluency in both - C++ on the hot path and Python around research - so list each with the kind of system you used it to build.
Pair engineering with quant domain: modern C++, Python (NumPy/pandas), multithreading and performance optimisation, Linux and build/CI on the engineering side; derivatives pricing, Monte Carlo, time-series, risk analytics, market microstructure and kdb+/q on the domain side. Add FIX/market connectivity and backtesting frameworks where they apply.
Name the instruments and methods you have actually worked with - derivatives pricing, Monte Carlo, time-series, stochastic calculus, market microstructure - and tie them to systems you built. Credentials like the CQF or FRM reinforce domain literacy, but worked examples of pricing or risk code carry more weight than the certificate alone.
One page early in your career, two pages once you have shipped systems worth describing. This audience rewards signal density over length, so use the space for systems, latency and PnL numbers rather than soft narrative, and cut anything that does not prove engineering or quant depth. If getting that density right for a senior desk or high-comp fund role feels high-stakes, you can have an expert writer shape it with you.
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