Senior Data Scientist & AI Advisor

My Role at DBFZ & KIDA

At the German Biomass Research Centre (DBFZ), I worked at the interface of applied data science, machine learning, and research infrastructure, including contributions to the interdisciplinary KIDA project. My focus was to help research teams translate complex data and computational questions into usable tools, reproducible workflows, and reliable analyses.

Key responsibilities

• Developed data and machine-learning workflows for applied research projects.
• Supported researchers in framing ML problems, choosing suitable methods, and evaluating results.
• Helped build and operate infrastructure for computationally demanding workloads, including Slurm-based HPC environments, container stacks, and GitLab/GitHub automation.
• Contributed to the planning and practical setup of shared AI/HPC infrastructure in the KIDA context.
• Worked on reproducibility, deployment, dependency management, and CVE-driven updates for research software environments.
• Developed open-source tools such as ScrAIbe and ScrAIbe-WebUI to make transcription and diarisation pipelines usable beyond purely technical users.
• Advised IT and research teams on translating prototypes and infrastructure ideas into more stable services.
• Supervised and supported student projects, theses, and internal knowledge transfer around programming, data science, and machine learning.

This work connected my physics background with practical AI and research software engineering: using machine learning not as an end in itself, but as a tool to make scientific work more transparent, reproducible, and useful.