Quantitative Trading Analyst

Maven Securities is a technology-driven proprietary trading firm trading its own capital across options market making, high-frequency, and multi-strategy discretionary strategies — from London, Amsterdam, and offices across the US and Asia. We combine deep trading, technology, and research expertise to improve liquidity across global listed derivatives, with AI embedded in how we operate day to day. 

As a Quant Trading Analyst within our Market Intelligence team, you will sit at the intersection of research, technology, and trading. You will turn raw trading data into high-value insights, build the analytics and AI tooling that Portfolio Managers (PMs) rely on daily, and deliver data-backed recommendations that materially and measurably improve PnL. This is a high-impact, high-ownership role for someone who wants their analysis to translate directly into trading performance.

• →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.
• →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.
• →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.
• →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.

• →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.
• →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.
• →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.

• 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.

• 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.