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Quantitative Trading & Research - Market Microstructure & High-Frequency Quantitative Researcher - Associate/ Vice President
mid · Technology / Software Development
The Quantitative Trading & Research (QTR) group is responsible for systematic trading across FX, Rates, Commodities, Credit, Equity and a wide range of markets. Within QTR, AI Market Lab brings together quantitative research, modern artificial intelligence, market microstructure, and high-performance engineering to develop the next generation of electronic trading capabilities. Our work spans signal research, pricing, market making, execution, portfolio construction, risk management, and the production systems that support them.
We are seeking a quantitative researcher with deep expertise in market microstructure and high- to medium-to-high-frequency trading to drive research on how electronic markets behave at fine time scales—and how that structure can be converted into robust, deployable systematic strategies.
This is a research-forward role. You will frame problems, build measurement and simulation machinery, run careful ablation studies, and develop models/strategies that hold up across venues, regimes, and operational constraints. The ideal candidate has worked close to live trading systems and can translate research insights into execution- and latency-aware designs.
Job Responsibilities
- Analyze high-frequency market data, including Level 2 and, where available, Level 3 or Level 4 order-book and order-event data, to identify predictive structure and trading opportunities.
- Develop alpha signals and trading features based on order flow, liquidity, queue dynamics, price formation, cross-venue behavior, and short-horizon market response.
- Design, backtest, and implement market-making and risk-taking strategies, including pricing, order placement, cancellation, queue-position management, fill-probability estimation, and inventory control.
- Develop realistic research and simulation methodologies incorporating latency, fees, rebates, market impact, adverse selection, and operational constraints.
- Optimize strategy performance across signal generation, portfolio or position sizing, execution, and intraday risk management.
- Work closely with traders, quantitative developers, technology partners, exchanges, and ECNs to move strategies into production and improve them using live performance and markout analysis
Required Qualifications
- Advanced degree or equivalent practical experience in mathematics, statistics, physics, computer science, engineering, financial engineering, or a related quantitative discipline.
- 2+ years of full-time quantitative research 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, microstructure invariants, liquidity formation, and market impact/adverse selection.
- Evidence of contributing to strategies used in live markets, including a clear understanding of the research-to-production workflow and the sources of performance degradation in deployment.
- Strong programming and data-analysis skills in Python; proficiency in C++ or another high-performance language is highly desirable.
- Demonstrated rigor in experimental design and evaluation—ability to separate economically meaningful effects from overfitting, leakage, optimistic fills, and regime-specific artifacts.
Preferred Qualifications
Experience independently owning a strategy, managing a trading book, or leading a quantitative research workstream.
- Deep expertise in one or more areas: high-frequency market making, short-horizon alpha, execution research, multi-venue routing/optimization, or microstructure modeling.
- Experience across FICC markets or multiple asset classes; outstanding equities specialists interested in transitioning to FICC are encouraged.
- Familiarity with machine learning, deep learning, or reinforcement learning applied to limit-order-book modeling, execution, or control problems.
- Research publications, open-source contributions, or substantial internal research artifacts demonstrating a sustained, hypothesis-driven approach.