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Head of Quant Risk Research

Job in Riverside, Connecticut, 06878, USA
Listing for: U.S. Bankruptcy Court - District of CT
Full Time position
Listed on 2026-02-28
Job specializations:
  • Finance & Banking
    Data Scientist, Mathematics
Job Description & How to Apply Below
Location: Riverside

Job Overview

A quant hedge fund is currently seeking their first Head of Quantitative Risk Research to join the team in Greenwich. They run systematic futures strategies across several markets, and this hire will be focused primarily on US Equity trading strategies.

First and foremost, this is a research-focused seat tasked with quantifying risk and assisting to identify orthogonal sources of alpha.

As a firm, this is a lean group of quants collaborating on their research efforts. They aim to solve complex problems, and ultimately, to identify and take advantage of market opportunities and inefficiencies. This new hire will be leading all research, design, development, and implementation of the models used to define risk and optimize portfolios in order to maximize returns.

You will be the architect for firm-wide equity risk factor research, risk factor model development, covariance matrix enhancement, alpha signal isolation, portfolio construction and optimization, and performance attribution analysis. Risk management at the firm is integrated with front office quants, and you will work with them hand in hand to communicate findings and implement quantitative solutions that maximize upside.

Requirements
  • 10+ years of quantitative risk/quantitative research experience at a hedge fund or trading firm
  • Ph.D. or Master's in Mathematics, Physics, Statistics, Computer Science, or a similar quantitative discipline (must have expertise in multivariate statistics and linear algebra)
  • Deep knowledge of US Equity market structure, with previous experience focused on US Equity Stat Arb strategies at a hedge fund or trading firm
  • Experience with risk factor research and factor model development - expanding upon vendor models (Axioma, Barra) and building proprietary models from scratch
  • Expertise coding in Python (Num Py, Pandas, Sci Py)
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