The Trading Research team is focused on the quantitative analysis of trading performance and strategy development. This involves detailed transaction cost analysis (TCA), improving our systematic trading and automation capabilities, and supporting the needs of our trading desks. The research and analytics function is performed by a global team with members in New York, London, Hong Kong and Mumbai using a global trading analytics platform. We are looking for a talented VP-level candidate in London.

Job summary:

As an Investment Platform Trading Research Executive Director on the Asset Management Investment Platform Trading Research team you will be responsible for designing and implementing research projects, optimizing our trading platform and implementing trade analytics framework. You will lead cross functional projects for our investment platform and provide execution consultancy to our clients. 

Job responsibilities:

Quantitative Trading Research: Design and implement research projects using market data analysis to identify signals, optimize execution strategies, and enhance trading performance metrics.  Optimize Trading: Collaborate with traders to enhance our systematic trading platform using statistical models and machine learning techniques. Provide Execution Consultancy: Serve as a subject matter expert working with traders, portfolio managers, and researchers to develop optimal execution strategies based on quantitative analysis of market conditions, liquidity profiles, and venue performance to balance market impact and transaction costs. Implement Trade Analytics Framework: Create dashboards and tools to measure execution quality, transaction cost analysis (TCA), market impact, and slippage. Create bespoke and scheduled reporting. Lead Cross-functional Projects: Coordinate with traders, portfolio managers, and technology teams to implement trading analytics solutions that address business requirements.

Required qualifications, skills and capabilities: 

Advanced Python Programming: Strong proficiency in Python with experience in data analysis libraries (pandas, NumPy), visualization tools (matplotlib, seaborn). Financial Database Expertise: Experience with databases and high-performance queries for analyzing tick/market data and trading records. Trading Knowledge: Demonstrated experience in market-making, statistical arbitrage, or systematic trading roles with understanding of execution algorithms and trading signals. Technical Communication: Ability to translate complex quantitative findings into actionable trading insights for portfolio managers and trading desk leadership. Market Microstructure Knowledge: Expertise in any of equities, fixed income, FX, derivatives, and alternative assets. Version Control Systems: Hands-on experience with Git and collaborative development workflows. Applied ML: Experience implementing reinforcement learning, natural language processing, or deep learning techniques.