Senior Data Scientist & AI Advisor

Steering AI strategy for sustainable energy research

I lead applied data science projects at the Deutsches Biomasseforschungszentrum (DBFZ), helping research groups unlock value from complex bioenergy datasets. My role combines high-level advisory work with hands-on implementation across the machine learning lifecycle.

• Design data and machine learning strategies that align with project goals, funding requirements, and ethical guardrails.
• Build and maintain reproducible analytics pipelines that empower researchers to iterate quickly without compromising rigour.
• Shape the institute’s AI-HPC roadmap, selecting tooling and infrastructure that balance performance, cost, and long-term maintainability.
• Mentor interdisciplinary teams on best practices in data governance, documentation, and collaborative experimentation.
• Introduce probabilistic reasoning methods and calibration routines that raise confidence in model predictions for policy-facing research.
• Lead the operation of Slurm clusters, container stacks, and GitLab/GitHub automation, including CVE-driven patching and security monitoring.
• Build open-source tooling such as ScrAIbe and ScrAIbe-WebUI so non-coders can access transcription and diarisation pipelines.

This position allows me to bridge theoretical physics training with real-world impact, ensuring that machine learning accelerates rather than obscures scientific insight.