We have a current opportunity for a Quant Developer on a permanent basis. The position will be based in London. For further information about this position please apply.

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.

Requirements

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
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 managementNice 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 disciplineWhat We're Looking For

You do not consider a model done until it is live, monitored, and generating PnL. You stress-test your own assumptions before anyone else does, explain a vol surface to a trader and a codebase to an engineer with equal fluency, and are genuinely energised by markets that are hard to model. ETrading Client trades at the sharp end of global energy derivatives. That should excite you.