Global Banking & Markets – GSET – Quantitative Strategist – London – VP

Overview

In this role, you will contribute to the Algo R&D team developing and refining electronic execution algorithms for cash equities. You’ll combine market microstructure expertise, quantitative modeling, and ML to improve execution quality at scale. You’ll work closely with traders and technologists to productionize research and monitor live performance. The position offers access to extensive market data, advanced infrastructure, and collaboration across global experts, with a strong emphasis on innovation and impact.

Responsibilities

• Enhance execution algorithms (VWAP, Participate, adaptive/liquidity-seeking) for cash equities
• Conduct rigorous quantitative research on market microstructure, order-book dynamics, venue analysis, and transaction cost analysis (TCA)
• Build and maintain statistical and machine learning models for short-term price predictions, fill-rate estimation, market-impact modeling, and optimal order placement/scheduling
• Collaborate with technology teams to productionize research into low-latency, high-reliability trading systems
• Perform back-testing, simulation, and live A/B testing of algorithm enhancements; define and track performance metrics
• Analyze large-scale tick data to identify alpha opportunities and areas for algo improvement
• Partner with sales, trading, and client-facing teams to translate client feedback and business requirements into research priorities
• Stay current with academic literature, regulatory changes (MiFID II) and competitive landscape in electronic trading
• Present research findings and strategic recommendations to senior stakeholders and cross-functional partners

Key requirements

• Advanced degree (Master’s or PhD) in a quantitative discipline
• 5+ years of experience in quantitative research related to execution/trading algorithms
• Deep understanding of market microstructure concepts
• Proficiency in statistical modelling, time-series analysis, and/or ML applied to financial data
• Proficiency with large datasets (tick data, order-book snapshots)
• Solid grasp of transaction cost analysis (TCA) methodologies and execution benchmarks
• Excellent communication skills to convey complex quantitative concepts to diverse audiences

• Collaborative mindset
• Impact-oriented approach
• Continuous learner
• Python programming
• kdb+/q data querying
• Reinforcement learning or deep learning techniques for execution problems

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