A systematic investment manager that has traded global futures for over a decade, and more recently expanded into U.S. equities, is seeking a Quantitative Researcher to lead research on proprietary covariance and portfolio-risk models used to construct portfolios across its strategies. This is an investment-research role, not risk oversight or vendor-model administration.

Researchers are viewed as core to the investment process rather than a support function, working directly with the CIO and partners and seeing their work tested in live trading. The team is small and lean, so candidates should be comfortable owning research end to end, from methodology through implementation, and contributing to related portfolio-construction and alpha-monetization problems.

Key Responsibilities

* Develop and improve methods for estimating covariance, correlation, and volatility across futures and equity universes, including how histories are selected and weighted, how estimates are shrunk and conditioned, and how models handle missing data, new instruments, and changes in the investment universe.

* Establish baselines and compare methods using predicted versus realized portfolio volatility, estimation error, matrix conditioning, eigenstructure, and resulting portfolio behavior, determining when evidence supports changing the current methodology.

* Measure how covariance estimates affect position sizes, diversification, concentration, and turnover, identifying changes attributable to estimation noise and investigating methods that reduce avoidable turnover.

* Own the research code, empirical tests, diagnostics, and model recommendations, carrying work through implementation and evaluating results against research predictions.

Requirements

* Two to ten years of hands-on quantitative research experience in finance, such as systematic strategy research, derivatives, futures, equities, risk estimation, or portfolio construction.

* Practical experience testing covariance, volatility, correlation, or related portfolio-risk estimates and measuring their effects on position sizing or portfolio construction.

* A PhD in statistics, mathematics, physics, electrical engineering, econometrics, operations research, or a comparably quantitative field required.

* Strong command of multivariate statistics, time-series estimation, and numerical linear algebra, including the assumptions, limitations, and failure modes of high-dimensional covariance methods.

* Strong Python and the ability to write tested, maintainable numerical research code; MATLAB or C++ experience useful but not required.