L/S Equity - Sector Data Science

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 highly motivated and technically strong Data Scientist to join the centralized Long/Short Equity team at a leading multi-strategy hedge fund. This team partners directly with portfolio managers and analysts across the L/S Equity business to identify, evaluate, and deploy data-driven insights that enhance investment decision-making and alpha generation.

The ideal candidate will combine strong technical and analytical capabilities with a practical understanding of financial markets. This role will focus on sourcing, analyzing, and operationalizing alternative and traditional datasets, building research tools, and developing scalable analytical frameworks that support discretionary equity investing.

Key Responsibilities:

• Analyze large structured and unstructured datasets to identify predictive signals and investment insights for L/S Equity portfolio managers.
• Source, evaluate, and onboard alternative datasets relevant to equity investing, including consumer, transactional, web, geolocation, sentiment, and fundamental datasets.
• Work closely with portfolio managers, analysts, and sector teams to understand investment processes and develop tailored data-driven solutions.
• Apply statistical techniques and machine learning methods where appropriate to improve signal generation, company analysis, and portfolio insights.
• Create dashboards, visualizations, and reporting tools that enable PMs and analysts to consume data effectively and make faster investment decisions.
  
  

Required Qualifications:

• 2+ years of experience in data science, quantitative research, or data analytics within a hedge fund, asset manager, investment bank, or technology-focused environment.
• Strong proficiency in Python, including experience with libraries such as pandas, NumPy, scikit-learn, and related data science tools.
• Experience working with large datasets, SQL databases, APIs, and modern data processing frameworks.
• Understanding of equity markets and investment workflows, ideally within a long/short equity investing environment a plus but not required
• Strong problem-solving and critical thinking abilities with a demonstrated ability to derive actionable insights from complex datasets.
• Ability to communicate findings clearly to both technical and non-technical stakeholders, including portfolio managers and investment analysts.
• Exposure to generative AI tooling for investment research workflows.
• Knowledge of software engineering best practices, including version control and CI/CD workflows.