We are seeking a Quantitative Researcher to join a collaborative systematic equities team in London, focusing on delta-1 trading, crowding & positioning strategies, and alternative data-driven research. The role involves working alongside a Senior Portfolio Manager (SPM) to develop and enhance systematic investment strategies through data-driven alpha generation and portfolio optimisation.
Principal Responsibilities
- Conduct alpha research with a primary focus on crowding, positioning, and liquidity-driven strategies in delta-1 markets.
- Develop and refine systematic trading signals by analysing alternative data-sets, market positioning trends, and fund flows.
- Implement statistical and machine learning models to extract actionable insights from structured and unstructured data sources.
- Collaborate with the SPM in strategy design, portfolio construction, and execution optimisation, ensuring strategies are scalable and robust.
- Backtest and validate models across multiple equity markets, ensuring integration into live trading frameworks.
Preferred Technical Skills
- Strong programming and research skills in Python.
- Experience working with large datasets, including alternative data, positioning data, and crowding analytics.
- Strong quantitative/statistical modelling background, with an understanding of market microstructure and liquidity dynamics.
Preferred Experience
- 3-5 years of experience in a systematic equity research role, ideally focused on delta-1 trading, crowding/positioning analytics, or alternative data research.
- Experience in statistical arbitrage or factor-based equity strategies.
- Strong understanding of market structure, liquidity, and flow-driven dynamics.
Highly Valued Relevant Experience
- Strong economic intuition and critical thinking, with the ability to translate complex datasets into alpha-generating signals.
- Prior experience building systematic strategies within a hedge fund, prop desk, or quant asset manager.
- Product experience in event-driven, sentiment-based, or alternative data models.
Target Start Date
As soon as possible.