Quant Developer (Python) - Equities StatArb

Senior Quant Developer (Python) - Equities StatArb - London - Up to £500k+ TC

Custom-built, high-performance Python DAG framework powering everything from research to live execution. Same codebase, same infrastructure, zero friction between backtesting and production P&L.

A global systematic investment manager ($6bn+ AUM) is expanding their Equities StatArb platform. Live since 2021, trading 24/6 globally.

Unified graph-based framework (DAG) across research, production, analytics, risk, and P&L. Deep Numpy/Numba work, processing vast historical datasets and real-time tick data. You'll contribute to shared tooling that researchers actually rely on.

You'll sit between researchers and the live trading desk, designing and owning critical pieces: portfolio construction, execution optimisation, transaction cost analysis. Direct line of sight to P&L.

Brand new City offices. Open floor plan, non-siloed. 60% of staff have 6+ years tenure. Technology is front-line, not a cost centre-real scope for mobility between desks, strategies, and business lines., * Building and supporting complex StatArb strategies across large-scale data processing, modelling, portfolio construction, and execution

• Contributing to the design of the DAG framework used across the entire stack
• Working closely with researchers to build tooling, shared libraries, and frameworks that they actually rely on
• Taking ownership of system components in a fast-paced, agile setup

Compensation is genuinely uncapped, this could go north of £500k TC for the right person. No fixed budget, just a question of fit and impact.

We encourage you to apply even if you don't tick every box, proven academics, willingness to learn, and genuine intellectual curiosity count for a lot here. Note: if you haven't received a reply within 3 days, your application was unfortunately not accepted.

• 5+ years as a Quant Dev in a systematic hedge fund (StatArb/equities experience ideal)
• Deep Python expertise (Numpy/Numba)
• Strong grasp of statistical methods, numerical optimisation, and equity market microstructure
• Experience with Unix internals and graph-based data processing frameworks