Quantitative Trading & Research – Market Microstructure & High-Frequency Quantitative Researcher – Associate or Vice President

Overview

In this role within the AI Market Lab of the Quantitative Trading & Research group, you frame market microstructure problems, build measurement and simulation tools, and conduct rigorous ablation studies to develop robust, deployable strategies. You translate insights from research to latency-aware execution designs that perform across venues and regimes. You’ll work at the intersection of quantitative research, AI, and high-performance engineering to advance electronic trading capabilities. This is a research-forward, production-focused opportunity with meaningful impact on how markets are analyzed and traded.

Responsibilities

• Analyze high-frequency and order-book data (Level 2/3/4 where available) to uncover predictive structure and trading opportunities
• Develop alpha signals and features from order flow, liquidity, queue dynamics, price formation, and cross-venue behavior
• Design, backtest, and implement market-making and risk-taking strategies including pricing, order placement, cancellation, and inventory control
• Create realistic research and simulation methodologies accounting for latency, fees, rebates, market impact, adverse selection, and operational limits
• Optimize performance across signal generation, portfolio sizing, execution, and intraday risk management
• Collaborate with traders, developers, exchanges, and ECNs to move strategies to production and improve via live performance and markout analysis

Key requirements

• Advanced degree or equivalent practical experience in a quantitative discipline (math, stats, physics, CS, engineering, financial engineering)
• Relevant experience in high-frequency/medium-frequency trading, electronic market making, or systematic execution
• Strong understanding of electronic market mechanics, order types, matching engines, queue priority, and market impact
• Evidence of strategies used in live markets and knowledge of research-to-production workflow and deployment degradation
• Strong Python programming and data-analysis skills; proficiency in C++ or another high-performance language is highly desirable
• Rigorous experimental design and evaluation to distinguish meaningful effects from overfitting and regime artifacts

• Collaborative mindset
• Rigorous and hypothesis-driven thinking
• Ability to translate research into production concepts
• Python
• C++
• Quantitative modeling

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