Verition Fund Management LLC ("Verition") is a multi-strategy, multi-manager hedge fund founded in 2008. Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

We are seeking a Senior Quantitative Researcher with strong experience in commodities futures to lead alpha research within a pod structure. The role requires ownership of the end-to-end research lifecycle , from signal generation and backtesting through deployment and live monitoring. Success in this environment requires a strong balance of research creativity, engineering discipline, and an understanding of the constraints of a production trading platform.

Responsibilities:

• Research, develop, and deploy systematic trading strategies across global commodities futures markets.
• Generate alpha signals using time-series, cross-sectional, and regime-aware approaches.
• Own signal performance from research through live trading, including ongoing evaluation and refinement.
• Build and maintain Python-based research infrastructure aligned with the pod's production systems.
• Ensure research is reproducible, scalable, and designed with deployment constraints in mind.
• Work closely with quant developers to transition research into robust, production-ready models.
• Contribute to portfolio construction, capital allocation, and risk management frameworks within the pod.
• Incorporate liquidity, transaction costs, and capacity considerations specific to futures markets.
• Monitor live strategy performance, investigate deviations, and iterate on models as market conditions evolve.
• Partner with the PM to make data-driven decisions on capital allocation and strategy scaling.

Qualifications & Experience:

• 4+ years of experience in quantitative research or systematic trading within a multi-manager or pod-based environment.
• Demonstrated success generating alpha in commodities futures.
• Strong proficiency in Python for research, backtesting, and analytics.
• Deep understanding of commodities futures mechanics, including rolls, term structure, seasonality, and contract specifications.
• Experience working within production constraints typical of multi-manager platforms (risk limits, turnover, capital usage).
• Experience deploying strategies into live trading systems.
• Familiarity with execution and transaction cost modeling for futures markets.
• Exposure to machine learning techniques applied to futures or time-series data.
• Strong communication skills and comfort operating in a lean, high-ownership pod structure.