Maven - Quantitative Trading Analyst

• Improve returns on existing strategies - Analyse performance at trade and strategy level, deliver data-backed recommendations to PMs, and maintain follow-through to implementation and outcome measurement. Success is quantifiable, recorded, and tied to material PnL improvement - not one-off analysis. • Improve returns on existing strategies • Build analytics infrastructure & AI tooling for PMs - Develop and standardise tools that generate high-value insights, and establish an AI foundation that accelerates PMs' day-to-day decision-making, working with Support and Technology teams where appropriate. • Build analytics infrastructure & AI tooling for PMs • Drive quantitative strategy research - Build a trading framework applied across multiple global strategies, identify style drift early and escalate to PMs and the HoMI, and document research in a reproducible format. • Drive quantitative strategy research • Create synergies and global engagement - Develop strong dialogue across the Market Intelligence team and build proactive, positive relationships with PMs that deepen engagement and adoption of market intelligence tooling. • Create synergies and global engagement • Develop advanced analytics infrastructure - Engineer scalable Python-based libraries and tools that standardise performance metrics across the group, and create automated workflows that convert raw trading data into insights for PMs and the Investment Committee. • Develop advanced analytics infrastructure • Conduct quantitative strategy research - Perform rigorous statistical analysis on trade- and portfolio-level data to decompose PnL drivers, and identify alpha decay, parameter inefficiencies, and execution drag to deliver actionable research. • Conduct quantitative strategy research • Improve returns on existing strategies - Identify inefficiencies and optimisation areas, deliver recommendations backed by data, and maintain follow-through to outcomes. • Highlight trading risks & opportunities - Monitor portfolio exposures, performance trends, and market signals to surface emerging risks and identify underutilised or mispriced opportunities across strategies. • Highlight trading risks & opportunities • Candidate Profile • Candidate Profile • Essential • 2–5 years of experience specifically within a Hedge Fund, Proprietary Trading Firm, or Quantitative Asset Manager. • Strong proficiency in Python (Pandas, NumPy, SciPy) for data analysis and simulation; SQL knowledge is essential. • Deep understanding of the trade lifecycle and buy-side strategies (e.g., Relative Value, Stat Arb, Volatility). • Outstanding numerical skills and excellent attention to detail. • Fast problem-solving skills and excellent communication and teamwork. • Highly valued • An understanding of how AI can support role-specific responsibilities while maintaining appropriate governance and compliance. • Proactive interest in improving existing strategies and identifying new opportunities. • Ability to take a high level of responsibility in an expanding, highly successful firm. • Demonstrable wider interest in financial markets.