Data Scientist

About the role

Join our Energy Data Science team to research, develop, and productionize time series forecasting models for energy markets. Your work will directly support energy trading decisions across one of Europe's largest residential energy communities and have measurable impact on how we participate in the energy market.

What you'll do

• Research, develop, and productionize time series forecasting models for energy markets (e.g. load, generation, and weather forecasting), owning the full lifecycle from data exploration and feature engineering through model training, evaluation, deployment, monitoring, and retraining.
• Contribute to our MLOps infrastructure: experiment tracking, model versioning, automated retraining pipelines, and production observability.
• Communicate forecasting performance and model insights to trading and product stakeholders, connecting technical results to business impact.
• Collaborate closely with Data Engineers, Software Engineers, and Product to deliver well-engineered, reliable data science products.
• Help shape our data science roadmap and contribute to the direction of next-generation forecasting approaches.
• Mentor and support junior data scientists, fostering a culture of continuous learning and technical growth.

What you'll need

• University degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
• 3+ years of industry experience in machine learning with a focus on time series forecasting, or in data science applied to the energy sector or complex distributed systems.
• Proficiency in Python and SQL, with strong software engineering fundamentals: testing, code quality, version control, and CI/CD.
• Proven experience deploying and monitoring machine learning models in production.
• Practical experience with data engineering tasks: pipeline development, data quality management, and feature engineering at scale.
• Experience with cloud infrastructure (Azure, AWS, or GCP).
• Hands-on experience with MLOps practices and tooling: experiment tracking, model registries, automated retraining workflows, and CI/CD for ML.
• Fluent in English.

Nice to have

• Experience in the energy industry, particularly in energy trading, energy market structures, or virtual power plants.
• Deep experience with time series forecasting methods.
• Experience working with weather data or numerical weather prediction models.
• Experience with probabilistic forecasting or uncertainty quantification.

What they offer

• Hybrid working with flexibility to relocate abroad for up to 30 days a year through Workflex.
• Modern office in Berlin-Friedrichshain with height-adjustable desks, table tennis, and barista coffee.
• 29 + 2 vacation days, discounted Wellhub membership, and corporate benefits.
• Onboarding with welcome bag, buddy program, and team support.
• Transparent culture with short decision-making processes and open feedback.