Johannes Petrat is a Principal Data Scientist based in London with a decade of applied AI experience spanning synthetic environments, computer vision, autonomous systems, V&V and signal processing. He has led end-to-end delivery of cutting-edge R&D and operational AI programmes—translating prototypes into frontline tools for defence, government and commercial clients. At Faculty he advises on AI adoption patterns and builds the engineering building blocks that reduce technical debt and ease transition from research to production. His background includes technical leadership on geospatial and probabilistic simulation at Improbable and hands-on work across streaming architectures, SAR satellite imagery and clickstream analytics. Trained in mathematical modelling at Oxford and experienced in cross-functional delivery, he combines rigorous quantitative thinking with pragmatic systems design to get models working reliably in live systems. An uncommon thread in his work is pairing simulation-driven uncertainty modelling with practical deployment pipelines to improve decision-making under ambiguity.
8 years of coding experience
9 years of employment as a software developer
Master of Science (M.Sc.) Mathematical Modelling and Scientific Computing, Master of Science (M.Sc.) Mathematical Modelling and Scientific Computing at University of Oxford
Junior Student, Junior Student at University of Hamburg
Bachelor of Science (B.Sc.) Applied and Computational Mathematics, Bachelor of Science (B.Sc.) Applied and Computational Mathematics at Jacobs University Bremen
Semester Abroad Adv. Real Analysis II Optimization Risk Management Convex Optimisation, Semester Abroad Adv. Real Analysis II Optimization Risk Management Convex Optimisation at Stockholm University
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