Quantitative Researcher - Central Execution Desk | Tier 1 Prop Trading
About the role
A leading global systematic trading firm is looking for a Quantitative Researcher to join its Central Execution Team in New York. This research-heavy, high-impact role sits at the intersection of market impact modeling, transaction cost analysis, causal inference, experiment design, and optimization. You'll build models, tools, and decision systems that systematic trading teams and portfolio managers rely on to route, schedule, evaluate, and optimize orders across brokers, algorithms, venues, and asset classes globally. Every basis point saved in execution is a basis point of alpha preserved in the portfolio.
You'll partner closely with traders, PMs, quant developers, and engineers to turn research prototypes into robust, production-grade analytics used daily across the firm's trading desks.
Responsibilities
- Market impact, slippage, fill quality, and execution cost research across global markets
- Predictive models to explain and forecast execution outcomes
- A/B experiment design to validate real, measurable execution improvements
- Causal inference methods applied to trading and execution data
- Optimization models for execution objectives under real-world constraints
- Research into liquidity, inventory, and crossing-style analytics to improve portfolio-level outcomes
- Dashboards, simulations, and research tools that help traders and PMs make sharper execution decisions
Requirements
- 2-5 years of quantitative research experience (flexible for strong candidates) in systematic trading, prop trading, execution research, or market microstructure
- Strong Python skills for research, modeling, and simulation; C++/Rust a plus
- Solid foundation in statistics, time-series analysis, experiment design, optimization, and machine learning
- Comfort working with large, messy financial datasets
- Genuine curiosity about market microstructure and how trading actually works, not just theory
Qualifications
- Bonus: convex optimization, causal inference, or stochastic control experience
- Bonus: prior research writing or publications