Quantitative Trader - Execution Services
Job Specification: Quantitative Trader – Execution Services
Location: London – 20 GS
The Central Liquidity Strategies (CLS) business manages a range of portfolios and products designed to optimize the firm’s trading and execution approach by providing internal liquidity solutions for portfolio managers on both a risk and agency basis.
We are seeking a Quantitative Trader to assist with all aspects of running a systematic trading and internalization book. This is a hands-on role offering an entrepreneurial individual the opportunity to make an impact by managing one of the most unique risk books in Europe.
Your role will involve operating, developing, and driving excellence in the desk's suite of products, spanning the entire workflow—from liquidity provision to portfolio managers at various benchmarks, through portfolio management, to optimized sourcing of risk from the market.
As part of a small, highly collaborative global team, you will:
1. Operate and monitor the highly automated live trading system, ensuring excellent service by overseeing flows and performance, intervening when necessary.
2. Conduct quantitative research and development, analyzing large datasets, developing cutting-edge code, tools, and models, and leveraging large-scale compute infrastructure to optimize liquidity sourcing and provision both internally and externally.
Key Responsibilities:
3. Operate and enhance systematic trading workflows, ensuring high performance and reliability.
4. Provide liquidity solutions to portfolio managers and optimize risk sourcing from the market.
5. Analyze large datasets using a scientific approach to improve trading strategies and desk performance.
6. Develop and maintain tools, models, and infrastructure to support trading operations.
7. Collaborate with a global team to ensure seamless execution and service excellence.
Experience Required:
Candidates should have hands-on experience within a market-making, risk, or trading business, ideally in cash equities, and familiarity with the following concepts and techniques:
8. Portfolio risk measurement and modeling.
9. Toxicity profiling and alpha forecasting.
10. Automation, tooling, and maintaining data integrity/pipelines.
11. Market impact modeling.
12. Liquidity provision and market-making workflows, ideally in cash equities.
13. Portfolio construction and optimization.
14. Alpha combination and attribution.
15. Statistical metrics and dimensionality reduction.
16. PnL decomposition and explanation.
Technical Skillset Required:
17. Education: Degree in Computer Science, Physics, Engineering, Statistics, Applied Mathematics, or a related technical field; Master’s or Ph.D. preferred.
18. Programming: At least 4 years of professional experience with Python and/or KDB; experience with Java/C++ is beneficial.
19. Systems: High proficiency with Linux, Git, and CI toolsets.
20. Quantitative Skills: Strong grounding in statistical reasoning and standard machine learning techniques.
21. Learning Aptitude: Excellent ability to rapidly learn from both experience and academic literature in a dynamic, fast-paced environment.