Equities Quantitative Researcher (Mid-Frequency - Systematic Trading)

£300k - £400k Total Comp

Our client is a leading systematic trading firm that leverages cutting-edge quantitative research, technology, and data science to develop scalable investment strategies across global equity markets.

They are looking to hire an experienced Equities Quantitative Researcher to join a high-performing research team focused on alpha signal research, portfolio construction, and systematic investment strategies. This is an opportunity to work alongside some of the industry's strongest quantitative minds, taking ownership of research that directly influences live trading portfolios.

You'll have access to extensive datasets, world-class compute infrastructure, and the freedom to develop innovative ideas from research through to implementation.

Responsibilities

1. Research and develop predictive alpha signals across global equity markets.
2. Design, test, and improve systematic investment strategies using statistical and machine learning techniques.
3. Build and enhance portfolio construction and optimisation models.
4. Develop risk-aware portfolio structuring methodologies that maximise risk-adjusted returns.
5. Analyse large alternative and traditional datasets to identify new investment opportunities.
6. Evaluate signal robustness through extensive backtesting and out-of-sample validation.
7. Work closely with Quant Developers and Portfolio Managers to productionise research.
8. Improve research frameworks, data pipelines, and model performance.
9. Monitor live strategy performance and continuously refine models.

Required Experience

1. MSc or PhD in one of the following: Mathematics, Statistics, Physics, Computer Science, Engineering, Machine Learning, Quantitative Finance, Economics (highly quantitative)
2. Strong experience as a Quantitative Researcher within a similar environment.
3. Proven experience researching equity alpha signals.
4. Strong background in portfolio construction, portfolio optimisation, and portfolio structuring.
5. Experience developing systematic equity investment models.
6. Excellent understanding of: Cross-sectional factor models, Statistical arbitrage, Risk modelling, Optimisation techniques, Transaction cost modelling, Capacity analysis, Experience working with large financial datasets.

Technical Skills

1. Python (essential)
2. SQL
3. Pandas, NumPy, SciPy, Scikit-learn
4. PyTorch or TensorFlow (desirable)
5. Git
6. Linux

Experience with cloud computing, distributed research environments, or high-performance computing would be advantageous.