Martin Ingram is a Senior Data Scientist based in Munich with 11 years of experience blending physics, computer science and advanced statistics to build practical ML solutions. After degrees from Cambridge and Imperial and a PhD in statistics from the University of Melbourne, he researched efficient Bayesian methods and implemented them in Python using JAX and TensorFlow to exploit GPUs and automatic differentiation. He has a strong track record applying ML in industry—from computer vision and deep learning consulting to production analytics for rail travel at KONUX—while favoring interpretable Bayesian approaches but choosing random forests or neural nets when they’re the right tool. Notable projects include high‑precision sports-video tracking systems and automating rigorous R&D tests in optics, demonstrating an ability to turn research into reliable production software. Colleagues value him for combining theoretical depth with hands‑on engineering and a pragmatic focus on solving real-world problems.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Arts (B.A.), Natural Sciences (Physical), II.1, Bachelor of Arts (B.A.), Natural Sciences (Physical), II.1 at University of Cambridge
Master of Science (MSc), Computing Science, Distinction (82.8%), Master of Science (MSc), Computing Science, Distinction (82.8%) at Imperial College London
Contributions:2 releases, 7 pushes, 1 branch in 2 years 1 month
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