About Me
I'm an intellectually curious problem-solver with a PhD in machine learning (ML) from TU Berlin and 12+ years of experience translating complex business problems into well-architected software and AI solutions — creating clarity from concept to code.
I've worked across a variety of domains in research and industry contexts (see CV), thriving as an individual contributor, team lead, and consultant.
Get in touch if you'd like to collaborate!
When I'm not curled up on the couch with a good book and my two cats, I'm passionate about:
- Refactoring Socio-Technical Systems: Streamlining (i.e., simplifying, restructuring, and automating) processes and code to help companies scale and grow their business with ease and foster operational excellence. At alcemy, for instance, I halved customer data integration time (from 50 to 25 days) through targeted code refactoring and process improvements.
- Mentoring Junior Developers: Empowering others to code with confidence and clarity, through structured teaching efforts, 1:1 coaching sessions, and ongoing code reviews.
- Developing Software Products: Discovering user needs and building applications to address them, from design to deployment. Especially data-heavy web apps including MLOps pipelines to monitor for data drifts and retrain models automatically.
- Being a Data Detective: Discovering patterns in complex datasets, crafting insightful data visualizations to guide decision-making, and improving ML model accuracy. My sweet spot is tabular and time series data—like sensor measurements from the process industry for predictive quality applications—where deep domain understanding makes all the difference.
Furthermore, I strongly believe that contributing to open source & open knowledge benefits everyone, which is why I've written two free online books, Clarity-Driven Development of Scientific Software and A Practitioner's Guide to Machine Learning.