Quantitative Researcher – Market Impact Modeling

Millennium•New York, NY

2d•$160,000 - $250,000

About The Position

The Quantitative Researcher will work within Central Liquidity Strategies (CLS), part of Execution Services, which manages portfolios and products designed to optimize Millennium’s trading and execution approach. The team provides internal liquidity solutions to Portfolio Managers on both a risk and agency basis, combining quantitative research, trading, and engineering. The role focuses on researching, improving, and implementing market-impact models across equities and other asset classes. It involves estimating market impact from real-world data while addressing data biases, endogeneity, causal inference, and model robustness. The researcher will develop research, model-fitting, and analytics frameworks for experimentation and calibration, and partner with trading and engineering stakeholders to integrate models into portfolio construction. The role also requires clearly communicating research findings, assumptions, and limitations and translating business goals into quantitative research deliverables.

Requirements

Master’s or PhD in Physics, Mathematics, Operations Research, Statistics, Finance, Economics, Econometrics, Financial Engineering, Econophysics, or a related discipline.
Experience in quantitative research or quantitative trading, with a significant focus on market-impact research or market microstructure.
Significant experience analyzing real-world datasets and estimating market impact in practical settings.
Strong knowledge of relevant academic research and the challenges of data bias, endogeneity, causal inference, and robustness.
Excellent analytical and quantitative skills.
Proficiency in Python and kdb+/q.
Strong communication and collaboration skills, including the ability to explain research outputs and limitations and align research with business objectives.

Responsibilities

Research, improve, and implement market-impact models across equities and other asset classes, drawing on academic research and practical market experience.
Estimate market impact using real-world data while addressing data biases, endogeneity, causal inference, and model robustness.
Develop and enhance research, model-fitting, and analytics frameworks to support experimentation and model calibration.
Partner with trading and engineering stakeholders to integrate models into the portfolio construction process.
Communicate research findings, assumptions, and limitations clearly, translating business goals into quantitative research deliverables.