Filippo Bovo
Lead Machine Learning Engineer at Meta
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolFilippo Bovo is a Lead Machine Learning Engineer in London with a PhD in theoretical physics and a decade of experience building production ML systems across automotive, retail and telecom sectors. He has delivered forecasting, recommender and probabilistic models at scale—most notably price-forecast models for 20M+ vehicles that unlocked international expansion and saved £350k/year—while also building big-data pipelines with Python, Dask and AWS. Comfortable spanning research and engineering, he led teams, supervised MSc projects, and created a “Production Data Science” workflow used to operationalize models. His background in statistical physics informs a principled approach to uncertainty and probabilistic modelling, bringing scientific rigor to product-facing ML solutions.
10 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree, Physics, 97/110, Bachelor’s Degree, Physics, 97/110 at Università degli Studi di PadovaDoctor of Philosophy (Ph.D.), Theoretical Physics, Full Scholarship, Doctor of Philosophy (Ph.D.), Theoretical Physics, Full Scholarship at University of Birmingham
Italian, English
Github Skills (35)
pandas9
python9
scikit-learn9
dask9
pydata9
principal-component-analysis9
science9
betfair9
game-development8
high-performance8
parallel-computing8
machine-learning8
parallel8
scipy8
data-science8
Programming languages (5)
Github contributions (5)
FilippoBovo/production-data-science
Oct 2017 - Mar 2019
Production Data Science: a workflow for collaborative data science aimed at production
Contributions:51 commits, 2 PRs, 38 pushes in 1 year 5 months
pythonsciencedata-scienceproduction-data-sciencemachine-learning
FilippoBovo/robustats
Aug 2019 - Dec 2020
Robustats is a Python library for high-performance computation of robust statistical estimators.
Contributions:10 releases, 1 review, 42 commits in 1 year 5 months
pythonprincipal-component-analysisrobust-estimatorsestimatorsmode
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