Associate Quantitative Trading & Research, Systematic Trading

What you'll do

• Design and implement end-to-end signal research and deployment infrastructure for equity derivatives.
• Develop alpha signals and systematic strategies from initial idea generation through to production implementation.
• Model equity options and volatility dynamics, including surface arbitrage, term structure, skew, and event risk.
• Build robust backtesting, attribution, and regime analysis frameworks tailored to derivatives PnL drivers.
• Create models integrating fundamental, quantitative, and microstructure features using machine learning or heuristics.
• Partner with trading desks on alpha capture, hedging design, and position risk management.
• Develop reusable research libraries and standardized workflows for experimentation and monitoring across global teams.
• Leverage AI/ML tools to accelerate research and improve developer productivity in a production environment.

What we're looking for

• Candidates must have a strong quantitative background and practical problem-solving skills.
• Experience in signal research with market data, alpha capture, and risk warehousing is required.
• Proficiency in Python, KDB, C++, or Java in a commercial environment is required.
• Ability to analyze complex, large-scale, high-dimensionality datasets from various sources is required.
• Candidates must possess excellent verbal and written communication skills to engage stakeholders.
• A Master's or PhD degree in a quantitative field such as Computer Science, Mathematics, or Physics is preferred.
• 3 to 5 years of experience in finance, including market making, electronic trading, or derivatives pricing and risk management is preferred.
• Expertise in statistics, machine learning, and equity derivatives/volatility products is preferred.