Summary
Guillem Hurault is a Senior Data Scientist based in London with a PhD in Statistical Machine Learning and eight years’ experience translating research-grade methods into production ML systems across sports analytics, healthcare and energy. He combines deep expertise in Bayesian modelling, time-series forecasting and computer vision with a strong engineering background that delivers maintainable, tested and deployable solutions. Guillem has led teams and built end-to-end workflows and coding standards that pushed 10+ enterprise models to production, and he has a track record of publishing and supervising academic work at Imperial College. His applied projects range from pose estimation for horses and betting-market models to personalised eczema prediction and electricity price and PPA valuation. Comfortable straddling academia and industry, he currently holds dual roles in industry and as a visiting researcher, reflecting an ongoing commitment to cutting-edge methods and reproducible software. A detail often missed: he pairs rigorous Bayesian thinking with practical software engineering, ensuring models are both statistically principled and operationally robust.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Bioengineering, Statistical Machine Learning, Doctor of Philosophy - PhD, Bioengineering, Statistical Machine Learning at Imperial College London
Master's degree, Engineering, Master's degree, Engineering at Centrale Lyon
Scientific French Baccalaureat, Scientific French Baccalaureat at Lycée St Joseph Bruz
Licence de Sciences Economiques et de Gestion, Economics, Licence de Sciences Economiques et de Gestion, Economics at Université Lumière Lyon 2
MPSI-MP*, MPSI-MP* at Lycée Chateaubriand
French, English, Portuguese