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Job Description : Description As a C++ Quant Developer, you will be responsible for implementing Treasury Futures and Options on Treasury Futures Models within BondCalc. You will collaborate with the Clients quants and ensure rigorous testing and validation of the models. Responsibilities: Implement Treasury Futures Model in C++ as per Clients Quant Library standards. Expand existing test suites and validate model accuracy. Collaborate with quantitative analysts to translate mathematical models into production-quality code. Optimize and modernize analytics infrastructure for fixed-income securities. Maintain version control and ensure code quality through rigorous testing. Skills & Qualifications: Proficiency in C++ with experience in financial libraries (e.g., QuantLib). Strong knowledge of fixed-income products, particularly Treasury Futures & Options. Experience with numerical computing, financial modeling, and Monte Carlo simulations. Ability to write clean, efficient, and testable code. Familiarity with Git, CI/CD, and Agile development methodologies. Experience working with quant teams in a high-performance computing environment. Project Overview The candidate will work on the Treasury Futures Model Development project, which involves C++ implementation of Treasury Futures Models in collaboration with quant teams. The role focuses on integrating these models into a Quant Library, enhancing analytics infrastructure, and conducting extensive testing. Required Qualifications Experience working in investment banking or asset management environments. Familiarity with quant libraries and financial engineering concepts. Proven track record of delivering large-scale quantitative development projects. Focus: Mathematical modeling and developing pricing/risk models. Key Responsibilities: Designing and implementing mathematical models for pricing financial derivatives. Developing risk models for portfolio management, VaR (Value-at-Risk), and stress testing. Writing C++ code to prototype and implement financial models. Calibrating models to market data and ensuring statistical robustness. Working closely with traders, portfolio managers, and quant developers to translate models into trading strategies. Applying stochastic calculus, numerical methods, and PDEs for pricing complex derivatives. Skills Required: Strong C++ & Python for model implementation and data analysis. Mathematics & Finance: Stochastic processes, probability, linear algebra, and option pricing (Black-Scholes, Heston, SABR). Numerical Methods: Monte Carlo simulation, PDE solvers, finite difference methods (FDM), finite element methods (FEM). Data Science & Machine Learning (optional): Applying ML for signal detection in trading. Statistics & Optimization: Kalman filtering, regression models, convex optimization. Common Employers: Investment banks (Barclays, Deutsche Bank, Credit Suisse) Hedge funds (AQR, Bridgewater, DE Shaw) Quantitative research firms (WorldQuant, Jane Street)
