C++ Quant Developer - Remote - ref. o4013223
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 Client's quants and ensure rigorous testing and validation of the models.
Responsibilities:
Implement Treasury Futures Model in C++ as per Client's 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
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.