Data Scientist – Multi Strategy Hedge Fund – Selby Jennings job at London Full Time NEW

Our client, a leading systematic investment firm is looking for a Data Scientist in London to support their data research and analytics function. Sitting within a central data team, this role works directly with quantitative researchers, traders, and engineers, owning the full lifecycle of data from sourcing and ingestion through to analysis, modelling, and delivery into systematic strategies. The position is highly front-office aligned, requiring close interaction with trading desks to translate datasets into actionable insights and research outputs, with natural progression towards a research-focused data scientist path.

Responsibilities:

• Own full-cycle data sourcing, onboarding, analysis, and dataset development across structured and alternative data sources
• Build and maintain robust end-to-end data pipelines, including extraction, cleaning, transformation, and delivery workflows
• Partner with traders and researchers to analyse datasets and present actionable insights to support trading decisions
• Prototype and productionise data solutions in collaboration with engineering teams, improving scalability and automation
• Drive data-driven research projects with direct impact on systematic strategies and trading performance

Requirements:

• Strong Python programming experience with data libraries (Pandas, NumPy) and proven experience building data pipelines
• Experience working with financial market data, with strong understanding of instruments and market structure
• Background in a quantitative discipline (e.g. Mathematics, Physics, Engineering) at postgraduate level
• Exposure to commodities-adjacent markets (equities, credit, delta one, indices, ETFs) and ability to communicate insights to traders
• Experience working across the full data lifecycle, ideally within a buy-side or systematic trading environment

In addition, the firm has dedicated specialist teams aligned to specific datasets and domains, including:

• Index Data: Focused on index, benchmark, and ETF datasets, including constituent data, weightings, rebalancing schedules, and flows, requiring strong knowledge of index construction and index-linked products
• Fixed Income: Focused on fixed income instruments and datasets, supporting credit and rates strategies (inferred from broader team structure across asset classes)
• Pricing: Focused on building and scaling pricing datasets and infrastructure, working across structured and unstructured data sources to support systematic strategies and trading decisions

These teams operate within the same core framework but differ in dataset specialisation and domain expertise, providing multiple pathways for progression depending on market focus and research interests.