Philip Oedi is a Data Scientist based in Berlin with 10 years of cross-sector experience applying AI, statistical modeling and design-thinking to problems in public health, engineering and music. Trained at KIT, TU Berlin and Tsinghua and certified in Design Thinking at Hasso Plattner Institute, he bridges rigorous computational methods (R/Python/BigQuery) with human-centered product design. At the Robert Koch Institute he developed outbreak detection and forecasting pipelines and automated surveillance-quality monitoring, and now helps the German Federal Foreign Office operationalize AI and data literacy across departments. He has also translated consumer-analytics work at Universal Music into production-ready dashboards and models, and early research roles produced predictive toolboxes for materials science. Known for turning messy real-world data into actionable systems, he combines technical depth with a knack for building cross-functional teams and scalable analytics platforms.
10 years of coding experience
4 years of employment as a software developer
Bachelor’s Degree, Mechanical Engineering, Bachelor’s Degree, Mechanical Engineering at Karlsruhe Institute of Technology (KIT)
Design Thinking, Design Thinking at Hasso Plattner Institute
Master's degree, Computational Engineering Sciences, Master's degree, Computational Engineering Sciences at Technische Universität Berlin
Mechanical Engineering, Mechanical Engineering at Tsinghua University
Contributions:13 commits, 22 PRs, 23 pushes in 4 months
pytorchtime-seriessegmentation
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