Mid-Level Quantitative Researcher

Mid-Level Quantitative Researcher

Location: Stamford, CT

Team: Relative Value Volatility Strategies

Reports to: Deputy CIO

We are seeking a mid-level quantitative researcher to join our investment team focused on relative-value volatility trading strategies across equities, indices, fx, rates, and credit. The role will combine quantitative modeling, data engineering, and applied research, supporting semi-systematic trading initiatives.

The ideal candidate will have strong Python programming skills, experience with cloud-based data platforms (Snowflake preferred), and a background in volatility products, derivatives, or other complex instruments.

• Research and implement relative-value volatility strategies, including spread, curve, and cross-asset vol relationships.
• Build and enhance quantitative models for pricing, and signal generation.
• Analyze historical and real-time market data to identify dislocations and arbitrage opportunities.
• Work with large structured and unstructured datasets in Snowflake, ensuring data integrity and accessibility.
• Develop Python-based research and production tools for backtesting, trade simulation, and performance attribution.
• Collaborate across the team to translate research into executable strategies.
• Present research findings in a clear, concise manner to senior stakeholders.

• 3–6 years of experience as a quantitative researcher, strategist, or data scientist in a hedge fund, bank, or trading firm.
• Solid understanding of volatility products and derivatives (e.g., options, variance swaps, VIX futures, volatility indices).
• Proficiency in Python for research, modeling, and data pipelines.
• Hands‑on experience with Snowflake or similar cloud-based data warehouses.
• Strong quantitative background (statistics, econometrics, applied math, or financial engineering).
• Familiarity with time-series modeling, machine learning, or risk factor analysis.
• Ability to work in a fast-paced, collaborative, and entrepreneurial environment.

• Experience with systematic volatility strategies (relative value, dispersion, correlation, skew).
• Background in SQL, data engineering, and APIs for market data ingestion.
• Exposure to portfolio construction, PnL attribution, and risk modeling.
• Advanced degree (MS/PhD) in a quantitative field is a plus.