Key Responsibilities

• Take ownership of the development and optimisation of quantitative trading strategies for dispatching flexible assets across intraday, day-ahead, balancing, and ancillary service markets.
• Build in collaboration with other teams predictive models for price forecasting, asset availability, imbalance signals, and market spread identification to improve bidding and scheduling decisions.
• Own the trading strategy roadmap, identify, prioritise, and deliver new features and model improvements in iterative cycles aligned with business value.
• Collaborate closely with traders to validate hypotheses, back-test strategies against real P&L, and incorporate trader intuition into model design.
• Scale strategies across geographies and asset classes, adapting to local market rules, grid codes, and asset-specific technical constraints (e.g., degradation, ramp rates, state-of-charge).
• Design and maintain robust data pipelines that feed real-time and historical market, weather, and asset data into modelling and decision engines.
• Monitor live strategy performance, detect drift or anomalies, and implement rapid feedback loops for continuous improvement.
• Communicate results clearly to both technical and non-technical stakeholders, translate complex model outputs into trading insights and strategic recommendations.
• Stay current with developments, and state-of-the-art methods in ML/optimisation relevant to energy trading.

Your Profile

• MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research, or a related quantitative field.
• 3+ years of professional experience applying data science or quantitative modelling in an energy trading, energy tech, or commodity trading environment.
• Deep understanding of European electricity markets (EPEX, Nord Pool, or equivalent) including day-ahead, intraday continuous, and balancing mechanisms.
• Excellent programming skills in Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost, or similar); SQL and cloud-based data platforms.
• Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers) in a trading context.
• Strong experience with optimisation techniques (LP/MILP, stochastic optimisation) applied to asset scheduling or portfolio optimisation.
• Excellent communication skills: you can explain a complex model to a trader at 7 AM and defend your methodology in a technical review at 3 PM.
• Autonomous and self-driven: you take ownership of your roadmap items, push them forward without constant guidance, and know when to escalate.
• Strong team player: you thrive in a fast-paced, collaborative environment where traders, developers, and data scientists sit side by side.