Job Description

Systematic Hedge Fund. 6 months.

Not an execution role. Not infrastructure for its own sake. This is a research-facing Python contract where you'll be embedded directly with quant researchers improving the speed, flexibility and reliability of a core backtesting framework.

The deliverables: a new signal evaluation and attribution module, improved data access tooling, parallel backtesting with Dask or Ray and clean handoff code the permanent team can maintain.

This role sits at the heart of the alpha research process. You will be working shoulder-to-shoulder with quant researchers on signal construction, factor evaluation, and strategy validation. Candidates must have direct experience working within a systematic hedge fund, asset manager, or prop desk specifically within a quant research or strategy development function. Strong Python from fintech, data engineering, or commercial tech is not sufficient. If you have not previously worked inside a systematic investment process, this role is not the right fit.

What they need:

→ 4+ years Python within a systematic hedge fund, asset manager, or quantitative prop desk

→ Hands-on experience building or materially improving a backtesting framework used in live strategy research

→ Working knowledge of the systematic strategy development lifecycle - from signal ideation and factor research through to live deployment

→ Deep Pandas, NumPy, Polars

— Dask or Ray for parallel compute

→ Understanding of how research decisions (lookback periods, signal construction, execution assumptions) affect strategy performance

→ KDB+/q read access beneficial

Contract Details:

• Duration: 6 months

• Rate: £750–£950/day depending on experience