We’re looking for a Senior Quantitative Equity Research Analyst to lead the quantitative research behind our stock-selection models and help build a new generation of AI-powered equity research products.

You will be the primary quantitative research expert on a small, high-caliber team that brings together top-tier engineering and buy-side talent, including senior analysts with prior experience at firms such as Millennium, Point72, and KKR.

This is a hands-on individual contributor role with significant autonomy. You will cover investment research methodology, research prototypes, testing standards and model monitoring. Our engineering team will own production infrastructure and scalability, partnering with you to turn successful research into reliable systems.

You will operate as a research partner and lead the quantitative research behind High Quality Stocks, a long-only, factor-based stock-selection strategy designed around an approximately five-year ownership horizon. You will contribute to the development of both a quality framework and a valuation framework designed to identify high-quality businesses whose current prices offer attractive long-term return potential.

The quantitative model drives stock recommendations. Working closely with senior equity analysts, you will translate fundamental investment judgment into measurable hypotheses, systematic signals, ranking models, and decision rules. Analysts help shape and challenge the investment framework; you will own the methodology, empirical validation, and ongoing performance of our models.

What You’ll Do:

• Lead the quantitative research agenda behind High Quality Stocks.
• Improve and refine a quality framework and valuation framework for identifying attractive long-term equity investments.
• Partner with senior equity analysts to translate fundamental concepts and investment hypotheses into systematic signals and ranking models.
• Design rigorous back tests using point-in-time data, with appropriate controls for look-ahead bias, survivorship bias, overfitting, multiple testing, and regime dependence.
• Develop machine-learning models and frameworks to help improve performance of the strategy and discover new factors
• Evaluate factor performance across sectors, company sizes, market environments, time periods, and portfolio-construction approaches.
• Code research prototypes and apply statistical and machine-learning methods where they improve the quality or robustness of the investment process.
• Partner with equity data analysts and AI/ML engineers to validate financial data and turn successful research into scalable, production-quality systems.