Our client, a leading global hedge fund , is seeking a Quantitative Research Manager – Equities to lead a team of researchers developing and deploying systematic equity strategies across global markets. This is a front-office, hands-on leadership role for a seasoned quant who knows how to move from model design to live execution—and build teams that do the same.
You’ll guide a group of researchers and developers focused on alpha generation, portfolio construction, and execution optimization within a sophisticated, data-rich environment. This is an opportunity to shape strategy architecture, mentor next‑generation talent, and deliver measurable performance in one of the industry’s most respected systematic platforms.
Lead a team of quantitative researchers and data scientists focused on alpha research across equities (stat‑arb, factor, and multi‑horizon).
Oversee signal discovery, model validation, and productionization of new trading ideas.
Manage end‑to‑end research pipelines —from data sourcing and feature engineering to backtesting and live performance monitoring.
Collaborate closely with portfolio managers, engineers, and traders to refine models and drive real P&L impact.
Evaluate and implement new machine learning, NLP, and alternative data techniques to uncover differentiated signals.
Guide research into execution algorithms, transaction cost modeling, and portfolio optimization.
Develop and enforce best practices for data integrity, version control, and model documentation.
Provide strategic direction, mentorship, and technical guidance to a high-caliber quant team.
8–12+ years of experience in quantitative research or systematic equities trading , including team leadership.
Proven record developing and deploying alpha models that have demonstrated live profitability.
Deep understanding of market microstructure, equity factors, and portfolio construction methodologies.
Expertise in Python, C++, R, or MATLAB , and familiarity with distributed computing frameworks.
Advanced degree (PhD or Master’s) in Applied Mathematics, Physics, Computer Science, or Financial Engineering.
Strong grasp of machine learning, statistical inference, and time-series analysis.
Excellent communication skills—able to translate research outcomes into actionable trading insights.
Entrepreneurial mindset and collaborative leadership style.
Opportunity to lead a world‑class quant research team with autonomy, resources, and scale.
Exposure to multi‑billion AUM trading infrastructure , global data sets, and elite engineering support.
Collaborative, performance‑driven culture that rewards innovation and measurable results.
Competitive compensation with P&L participation and leadership upside.
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