Data Scientist Flex Trading Strategy Development (fmd)

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.