Quantitative Researcher - Execution Modeling

You will take on the following responsibilities:

- Optimize how orders are scheduled and how that schedule interacts with the tactics underneath it and with portfolio optimizers
- Improve our execution simulator and its agreement with production results, including fill probability, adverse selection, and post-trade impact
- Research and develop short-horizon signals from order book, quote, and trade data to improve trading tactics, order placement, and venue selection
- Build and improve market impact models across trading horizons, from seconds to multiple days, and across instruments with very different liquidity profiles
- Design and analyze production experiments (A/B tests) and execution performance metrics that separate real improvements from market noise
- Use our large internal history of orders and fills to find new sources of execution alpha
- Build relationships with other teams supporting investment and research processes, and work closely with multiple teams on joint research

You should possess multiple of the following qualifications:

- Excellent quantitative skills, as evidenced by formal training in statistics, applied mathematics, operations research, economics, computer science, physics, engineering, or a related quantitative field. A PhD is a plus but not required.
- 5-15 years of experience in execution research, algorithmic trading, market-making, or high-frequency trading at a financial firm.
- Deep understanding of market microstructure, including order types, venues, dark pools, queue dynamics, and how different market participants behave
- Hands-on experience with market impact modeling, transaction cost analysis, or execution simulation
- Expertise with large datasets of intraday market data (quotes, trades, order book). Experience with full order-by-order (L3) data is a significant plus.
- Experience applying machine learning techniques to trading, execution, or modeling problems
- Strong programming skills (Python, Java, or C++), data management and retrieval skills, and literacy with Linux and LLM support
- Effective communication skills, both written and verbal
- Ability to own research end-to-end, from data gathering through hypothesis testing to production deployment and monitoring, in a fast-paced, team-oriented environment