Quant Developer

Candidate will sit at the boundary between rigorous financial research and production engineering - writing models that run live on the trading floor, priced in real time, monitored in production, and generating PnL. You will work directly with traders and risk managers in a true front office environment where the feedback loop between your research and market outcomes is immediate. This is not a research lab role., * Real-time pricing and risk system design - latency-aware implementation, incremental recalculation, and feed-driven revaluation

• Backtesting framework design: walk-forward validation, statistical significance testing, and performance attribution
• Production-quality Python and/or C++ - code an engineer can review, a CI pipeline can test, and an ops team can support
• kdb+/q, In__DB, or TimescaleDB for high-frequency time-series market data storage and analysis
• Ability to set a research agenda independently and communicate risk and findings clearly to senior traders and management
• Cloud infrastructure - Azure preferred, AWS considered; IAM, managed services, automated and auditable deployment pipelines, secrets management

Nice to Have

• Market making, systematic execution, or electronic trading in energy or commodity derivatives
• Asian options, barrier structures, or path-dependent exotics common in commodity markets
• Machine learning applied to vol forecasting, regime detection, or execution cost optimisation
• Open-source quant library contributions, published research, or CQF/MFE/PhD in a quantitative discipline

• 10-15 years combined quant research and software engineering; minimum 5 years embedded in a front office (any asset class)
• Options pricing across the full surface - vanilla, spreads, and structured products in commodity or energy markets
• Vol surface calibration: smile fitting, SABR, SVI, Heston, or equivalent; arbitrage constraints and numerical stability in production
• Greeks and second-order risk: delta, gamma, vega, volga, vanna, theta; PnL attribution and daily risk reconciliation
• VaR, stressed VaR, and scenario analysis implementation; working knowledge of regulatory capital frameworks
• Commodity modelling: term structure, forward curve construction, seasonality, convenience yield, and basis risk