Python Quant Developer – Research Platform & Strategy Implementation | Systematic Hedge Fund | London

A leading systematic hedge fund is seeking a Python Quant Developer to build and maintain the research and strategy implementation platform that sits at the heart of their alpha generation process. This is a hybrid developer-researcher role for someone who writes clean, high-performance Python, understands quantitative finance, and takes pride in building robust infrastructure that researchers love to use.

The Opportunity:

You will work directly alongside quant researchers, building the libraries, frameworks and pipelines they depend on to research, backtest and deploy systematic strategies. Your code runs in production. Your architecture decisions shape how research is done.

Key Responsibilities:

• Design and build Python-based research and backtesting frameworks used by the entire quant research team

• Implement alpha signal pipelines: data ingestion, feature engineering, signal generation and evaluation

• Build strategy simulation and portfolio optimisation tooling with rigorous statistical analysis

• Develop data infrastructure integrating market, alternative and proprietary datasets

• Collaborate with researchers on signal implementation, performance attribution and live strategy monitoring

• Maintain production-grade code quality: testing, documentation, version control (Git), CI/CD pipelines

• Profile and optimise Python code for research pipeline throughput (NumPy, pandas, Dask, Numba)

Required Experience:

• 3–8 years of Python development in a quantitative finance environment (hedge fund, prop desk or asset manager)

• Strong Python: NumPy, pandas, SciPy, scikit-learn, and ideally Dask or PySpark for large-scale data

• Solid understanding of systematic trading concepts: signal research, backtesting methodology, transaction costs

• Experience with SQL and time-series databases (kdb+/q, InfluxDB, or similar)

• Git, Linux command line, and production software engineering practices

• Mathematics, Statistics, Computer Science or Physics degree from a top university

Highly Desirable:

• Experience with machine learning in a quant research context (sklearn, PyTorch, TensorFlow)

• Knowledge of options pricing, factor models or portfolio optimisation

• kdb+/q or Rust familiarity a plus