Senior Data Scientist (Energy Trading)

TL;DR

Senior Data Scientist (Energy Trading): Developing and optimizing quantitative trading strategies for flexible assets in European electricity markets with an accent on price forecasting, asset availability, and market spread identification. Focus on building predictive models, scaling strategies across geographies, and designing robust real-time data pipelines.

Location: Hybrid work model in Essen, Germany

Company

hirify.global Energy Markets GmbH coordinates access to trading markets for regional business units and provides innovative services in portfolio strategies and risk management.

What you will do

• Develop and optimize quantitative trading strategies for dispatching flexible assets across intraday, day-ahead, balancing, and ancillary service markets.
• Build predictive models for price forecasting, asset availability, and market spread identification to improve bidding decisions.
• Own the trading strategy roadmap, prioritizing and delivering new features in iterative cycles aligned with business value.
• Collaborate with traders to validate hypotheses, back-test strategies against real P&L, and integrate trader intuition into models.
• Design and maintain robust data pipelines for real-time and historical market, weather, and asset data.
• Monitor live strategy performance, detect anomalies, and implement rapid feedback loops for continuous improvement.

Requirements

• MSc or PhD in Data Science, Statistics, Mathematics, Physics, Computer Science, Operations Research, or a related quantitative field.
• 10+ years of professional experience in data science or quantitative modelling within energy or commodity trading environments.
• Proven track record of deploying data-driven solutions using MLOps tooling and CI/CD.
• Deep understanding of European electricity markets (EPEX, Nord Pool, or equivalent).
• Strong programming skills in Python (pandas, NumPy, scikit-learn, LightGBM/XGBoost) and SQL.
• Experience with reinforcement learning, Bayesian methods, or time-series deep learning (LSTMs, Transformers).

Nice to have

• Strong experience with optimization techniques (LP/MILP, stochastic optimization) applied to asset scheduling or portfolio optimization.

Culture & Benefits

• Hybrid work model and flexible working times.
• Flat hierarchies and a highly cooperative, interdisciplinary working style.
• Modern digital and ergonomic workplace environment.
• Life-long learning opportunities with access to state-of-the-art training.
• Subsidized bistro and canteen with a wide selection of fresh meals and health offers.
• Central location with excellent public transport and free parking/charging for e-vehicles.