Thomas Riley is a Principal Data Scientist with nine years of experience applying first-principles, statistical, simulation and ML techniques to fleet optimization, rebalancing and real-time dispatching in mobility operations. Based in Amsterdam, he transitioned from academic astrostatistics—where he led development of the X-PSI open-source framework and contributed central analyses to NASA’s NICER mission—to building production data services for a remotely operated car-sharing startup. He combines deep expertise in Bayesian computation, high-performance scientific code (Python/C/Cython/OpenMP) and geospatial-temporal modelling to turn research-grade methods into pragmatic, customer-valued solutions. Known for owning end-to-end systems, he bridges offline learning with low-latency real-time pipelines and collaborates across engineering teams to deploy robust operational models. An uncommon strength is his track record of translating complex, computationally intensive science software into scalable, documented tools used by both research and production teams.
9 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Astrostatistics, Doctor of Philosophy - PhD, Astrostatistics at University of Amsterdam
Master’s Degree, Astrophysics, 1st Class Honours, Master’s Degree, Astrophysics, 1st Class Honours at University of Birmingham
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