Joseph King is a Senior Data Scientist based in Munich with six years of hands-on experience building ML and causal inference solutions across tech, consulting, and government research. He has led teams and projects from federated survival models deployed to edge devices to production recommender systems and causal experiments for marketing, blending deep learning, survival analysis, and explainability techniques like SHAP. Joseph has a strong research pedigree (PhD-level work) applied to high-stakes domains such as defense and national statistics, where he scaled pipelines for hundreds of millions of records and studied systemic inequalities. He’s comfortable owning end-to-end systems—ETL, model development, cloud deployment (AWS IoT, Greengrass, Lambda), and MLOps—and has practical experience producing synthetic data and generative models to bootstrap tiny datasets. A technical lead who pairs rigorous causal thinking with pragmatic engineering, he uniquely bridges academic rigor and production-grade ML in regulated, real-world settings.
6 years of coding experience
14 years of employment as a software developer
Master of Arts (M.A.), Master of Arts (M.A.) at Washington State University
Bachelor of Science (B.S.), Bachelor of Science (B.S.) at University of Idaho
Non-Degree, Non-Degree at UC San Diego Extended Studies
Doctor of Philosophy (PhD), Doctor of Philosophy (PhD) at University of California, Irvine
Scrapes Google Images to create a ~700k sample of US passenger vehicle images, YOLOv5 to detect and crop vehicles in images, ResNet50 to classify vehicle make-models
Contributions:199 commits, 2 PRs, 38 pushes in 4 months
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