Quant Engineer

Paris | Permanent (CDI) | €75,000–85,000 base plus discretionary bonus The business An independent French energy company that develops, owns and operates grid-scale flexible assets and trades them on the power markets. Well funded, small team, and the whole stack built in-house rather than bought in – forecasting through to execution. The role You own electricity price forecasting. The models are already live, and they aren’t decision support: they feed the optimisation engine that decides when assets charge, discharge and bid. Forecast error shows up in the P&L the same day. Short horizons are the priority. Longer-range work exists and can be picked up over time. What you’ll do - Maintain and extend the live models, and design new approaches where the current ones fall short - Build ML and deep learning models alongside fundamental ones - Develop the scenario generators behind the price views, and put proper uncertainty around the central case - Turn market fundamentals and asset constraints into usable features - Work out what drives price and volatility, and quantify it - Improve the MILP dispatch modules, on economic performance and on speed - Track forecast error in production and act on it - Help harden the platform generally What they’re looking for - Three to five years forecasting, modelling or quantitative analysis in power markets - Excellent Python - Time series and applied ML or deep learning - Statistics, applied stochastic modelling, mixed-integer optimisation - A working understanding of how the market functions — day-ahead, intraday, balancing, ancillary services - Git, code review, CI/CD, and the instinct to take your own work into production - Master’s or engineering degree in energy, applied maths, data science, econometrics or computer science - Useful but not required: dispatch optimisation, systematic trading, battery storage. Practicalities Paris-based. Working language is French with English spoken alongside it; French preferred rather than mandatory. Why people take it Wide scope and real ownership in a small team. Models running against live assets rather than sitting in studies. Close proximity to how the business makes its capital decisions.