Quantitative Researcher to join New York City based team, focusing on building and expanding a next-generation U.S. equity and index options research and trading platform. The role involves end-to-end development of models, from idea generation to live execution, with a focus on alpha discovery, volatility modeling, and execution efficiency.
Key Responsibilities
• Develop and test systematic trading signals across equity and index options.
• Conduct volatility surface modeling and calibration.
• Optimize execution strategies through data-driven research.
• Collaborate with engineering teams to design scalable research infrastructure.
Required Qualifications
• Minimum 2 years of experience in quantitative research, predictive modeling
• Hands-on experience analyzing large-scale datasets.
• Deep understanding of options theory, including pricing, Greeks, and volatility dynamics, vol surface calibration, etc.
• Proficiency with Python and data science toolkit (e.g., Polars, DuckDB, LightGBM, Plotly, scikit-learn).
• Familiarity with LLMs or AI-assisted coding to improve code structure and maintainability.
• Demonstrated intellectual curiosity, persistence, and adaptability.
• Comfortable working independently while contributing effectively to team goals.
Preferred Background
• Advanced degree (Master’s or Ph.D.) in Data Science, Applied Mathematics, Computer Science, or a related quantitative discipline
• Strong understanding of ML methodology
• Experience deploying research workflows in distributed or cloud-based environments.
• Knowledge of equity risk modeling frameworks (e.g., Barra or equivalent)
