Energy 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. 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, Working understanding of power market functions (day-ahead, intraday, balancing, ancillary services), Experience with Git, code review, and CI/CD, Master’s or engineering degree in energy, applied maths, data science, econometrics or computer science