Public summary
Join a dynamic team driving the energy transition by developing and improving machine learning forecasting models for decentralized Energy Management Systems. Support data and ML pipelines, monitor production models, and contribute to autonomous AI workflows that optimize clean energy usage. Ideal for students enrolled in quantitative programs who want real-world experience in data science and renewable energy technology.
Location and work setup
- Location
- Berlin, Hamburg, Munich
- Remote status
- Unknown
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Support building, improving, and retraining time-series forecasting models for PV generation, household load, heat pump consumption, and EV charging. Monitor, update, and troubleshoot ML models and data pipelines in production. Assist with data cleansing and maintain large data streams using SQLMesh and BigQuery. Backtest forecast models using internal simulations and analyze forecast impacts on EMS optimization. Contribute to developing autonomous AI agents for workflow automation and maintain technical documentation and team collaboration during sprints.
Qualifications
Currently enrolled in a Bachelor's or Master's program in Data Science, Computer Science, Statistics, Mathematics, Physics, or related quantitative field. Strong Python programming skills and familiarity with data science libraries like pandas, numpy, and scikit-learn. Solid understanding of ML principles, statistical analysis, model architectures, and evaluation metrics. Analytical mindset with enthusiasm for troubleshooting and validating models. Experience or interest in cloud infrastructure, SQL queries, generative AI tools, data pipeline tools, and data visualization is a plus. Team-oriented with agile working experience and a keen interest in renewable energy and smart grid technologies.