L/S Equity - Sector Data Science

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 (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.