About
I work at the intersection of product and data. As a Product Owner for Data Engineering at idealo in Berlin, I focus on building data products, working with cloud infrastructure on AWS, and making sure technical work connects to what the business actually needs.
My background is in Computer Science, with research work in AI explainability. I care about building things that are useful, understandable, and well-designed.
This site is a place for me to experiment, write, and share ideas. Nothing here is meant as strong opinion or advice. It reflects things I find interesting and worth thinking about, and maybe some of it is useful to someone else too.
Outside of work, I follow developments in world politics, health and medicine, and environmental topics.
Product Owner, Data Engineering idealo · Berlin
- Building and shipping data products at scale
- Working on the data infrastructure we run on AWS
- Working across product and engineering teams
- Data Products
- Analytics
- Machine Learning
- Agentic Applications
- AWS
- Product Management
- AWS
- AI
Computer Science TU Berlin · Trier University of Applied Sciences
Master’s thesis on explainability in neural text classification for e-commerce. I evaluated five attribution methods using Sensitivity-n: Gradient Input, Integrated Gradients, SHAP, LRP, and DeepLIFT. DeepLIFT and Integrated Gradients performed best, and the results showed that model decisions are driven by a small subset of features.