Job Description

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.

What You’ll Do

• 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.

What You Bring

• 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.

Why It’s Worth a Conversation

• 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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