EE
Senior Data Scientist - Flex Trading Strategy Development (f/m/d)
E.ON Energie Deutschland GmbH
Essen · flexible Senior 1mo ago
About the role
About
E.ON Energy Markets GmbH is seeking a Senior Data Scientist – Flex Trading Strategy Development to join our team in Essen.
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, translating 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.
- 10+ years of professional experience applying data science or quantitative modelling in an energy trading, energy tech, or commodity trading environment.
- Proven track record of developing and deploying IT/data‑driven solutions that directly support trading decisions or automated dispatch using MLOps tooling and CI/CD for model deployment.
- 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, Num Py, scikit‑learn, Light GBM/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 escalated.
- Strong team player: you thrive in a fast‑paced, collaborative environment where traders, developers, and data scientists sit side by side.
Benefits
- Flexibility: hybrid work model, flexible working times allowing great compatibility with your studies.
- Flat hierarchies: interdisciplinary and very cooperative working style providing room for own ideas.
- Career entry: find out about our job vacancies first‑hand and make valuable contacts – we promote our young talents.
- Modern work environment: workplace according to digital and ergonomic standards.
- Personal growth: lifelong independent learning using a broad range of opportunities working with the newest technology and state‑of‑the‑art trainings.
- Nutrition & Health: wide selection of fresh meals and drinks in our subsidised bistro and canteen as well as various health offers (e.g. physiotherapy, flu vaccinations, mental health).
- A central location: very good public transport connection, free parking and charging points for e‑vehicles.
Skills
Bayesian methodsCI/CDEPEXLSTMsLight GBMMILPMLMLOpsNord PoolNumPyOptimizationPandasPythonReinforcement learningSQLScikit-learnStochastic optimisationTransformersXGBoost
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