Quantitative Researcher - Central Execution Desk | Tier 1 Prop Trading

A leading global systematic trading firm is looking for a Quantitative Researcher to join its Central Execution Team in New York, working alongside a global research team focused on protecting and enhancing alpha at the point of execution. This is a research-heavy, high-impact role sitting at the intersection of market impact modeling, transaction cost analysis, causal inference, experiment design, and optimization. You won't just be analyzing execution after the fact - you'll be building the 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. Think of it as alpha preservation research: every basis point saved in execution is a basis point of alpha that stays 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. What you'll work on: 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 What we're looking for: 2-5 years of quantitative research experience (flexible for strong candidates) - systematic trading, prop trading, execution research, or market microstructure backgrounds all welcome 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 Bonus: convex optimization, causal inference, or stochastic control experience, and prior research writing or publications This is a rare opportunity to do rigorous, well-resourced quant research with direct, visible impact on trading performance - without the ego or bureaucracy of a larger, siloed organization.