Thomas Pinder
Senior Data Scientist
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolThomas Pinder is a Senior Data Scientist with nine years’ experience applying Bayesian causal inference and Gaussian process methods to high-impact product and marketing experiments across major tech firms. Currently helping shape content and studio measurement at Netflix, he also leads PyMC Labs work as a principal data scientist and founders JAXGaussianProcesses, an open-source effort that optimises GP computation for scalable modelling. His career includes accelerating Amazon supply-chain emulation 7–14x with GP-based emulators and building attribution frameworks for EU YouTube campaigns, showing a rare blend of academic rigor (PhD in Statistics) and production impact. Curious about efficient inference, he focuses on approximate and causal methods that deliver actionable insights rather than just predictive accuracy.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BSc Mathematics and Statistics, Bachelor of Science - BSc Mathematics and Statistics at University of ReadingDoctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at Lancaster University
English
Github Skills (90)
probabilistic-programming10
mcmc10
pyro10
uncertainty10
bayesian-inference10
optimisation10
jax10
julia10
cpu10
gaussian-processes10
automatic-differentiation10
diffraction10
compilation10
machine-learning10
uncertainty-quantification10
Programming languages (5)
Github contributions (5)
JaxGaussianProcesses/GPJax
Sep 2020 - Jan 2023
Gaussian processes in JAX.
Contributions:36 releases, 212 reviews, 586 commits in 2 years 4 months
bayesian-inferencemachine-learningprobabilistic-programminggaussianresearchers
JaxGaussianProcesses/JaxKern
Nov 2022 - Mar 2023
Kernel functions in JAX.
Contributions:1 release, 17 reviews, 87 commits in 4 months
kernelkernel-functionsjax
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