Exotic Equity Derivatives Quantitative Analyst

Camber Morris are working with an elite multi strat hedge fund who are expanding their front-office quantitative analytics capabilities to support their growing global equity derivatives franchise. We are seeking a talented Equity Exotics Quantitative Researcher with deep expertise in autocallables and complex structured products to join our dynamic team. In this high-visibility role, you will be instrumental in designing, implementing, and optimizing the pricing models, risk management frameworks, and hedging strategies that drive our exotic equity business. Working side-by-side with portfolio managers, you will directly influence daily trading decisions and the strategic expansion of our product suite.

• Key Responsibilities
• Develop, refine, and deploy advanced mathematical pricing models and numerical algorithms tailored specifically to equity exotic derivatives, with a primary focus on autocallables, barrier options, and multi-asset structures.
• Collaborate directly with the equity exotics trading desk to provide real-time quantitative support, risk analysis, and custom pricing solutions for complex structured transactions.
• Enhance Monte Carlo simulation frameworks, local/stochastic volatility models, and partial differential equation (PDE) solvers to improve computational speed, precision, and risk sensitivity.
• Formulate and backtest innovative, systematic hedging strategies to effectively manage complex cross-Greeks and residual risks inherent in exotic product portfolios.
• Work alongside quantitative developers to integrate robust models into high-performance production C++ and Python analytics libraries.

• Required Skills & Experience
• Proven track record as a Quantitative Researcher or Financial Engineer within a front-office Equity Derivatives or Structured Products business.
• Extensive hands-on experience in pricing, risk-managing, and modelling equity exotic structures, with demonstrable, deep domain expertise in autocallables.
• Superior theoretical foundation in stochastic calculus, numerical methods, probability theory, and quantitative finance techniques (e.g., Dupire local volatility, SABR, Heston, path-dependent Monte Carlo).
• Advanced programming proficiency in production-grade C++ and Python, with a strong commitment to clean architecture and performant code.
• Master’s degree or PhD in a quantitative discipline such as Financial Mathematics, Applied Mathematics, Theoretical Physics, Computer Science, or Engineering.

• Nice-to-have
• Direct exposure to multi-asset hybrid products, dividend risk modelling, or repo and correlation trading dynamics.
• Practical understanding of modern machine learning techniques applied to model calibration, risk approximation, or market regime detection.