Guillaume Godin
Principal Machine Learning Engineer at Osmo
Geneva, Geneva, Switzerland
Summary
👤
Senior🎓
Top SchoolGuillaume Godin is a Principal Machine Learning Engineer and scientific leader with over a decade of experience applying AI to chemistry, biology, and sensory sciences, now based in Geneva. He blends a PhD-level chemistry background and formal ML training to drive product-focused research in language models, neural networks, and omics across cosmetics and consumer insights. Known for translating cutting-edge methods into real-world systems, he has led AI teams at Firmenich and dsm-firmenich and now shapes ML strategy at Osmo. An active open-source contributor, Guillaume has meaningfully improved RDKit’s fingerprinting and atom-feature tooling—work that underpins many chemoinformatics pipelines. He combines stakeholder-facing collaboration with deep technical craftsmanship, making him adept at turning complex scientific problems into deployable ML solutions.
11 years of coding experience
17 years of employment as a software developer
Engineer's degree, Chemistry, Engineer's degree, Chemistry at Ecole Nationale Supérieure de Chimie de MulhousePostgraduate Degree, Organic Chemistry, Postgraduate Degree, Organic Chemistry at University of GenevaDoctor of Philosophy - PhD, Organic Chemistry, Doctor of Philosophy - PhD, Organic Chemistry at Université d'Orléanscontinuous training machine learning, Applied Data Science: Machine Learning, continuous training machine learning, Applied Data Science: Machine Learning at EPFL Extension School
French, English
Github Skills (7)
descriptor10
rdkit10
c-language10
cprogramming-language10
cheminformatics10
python9
computational-chemistry9
Programming languages (15)
Github contributions (5)
rdkit/rdkit
Mar 2018 - Mar 2020
The official sources for the RDKit library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits, 29 PRs, 146 comments in 2 years
Contributions summary:Guillaume contributed significantly to the RDKit library, specifically focusing on the development of chemical descriptors and fingerprinting functionalities. They implemented updates to the Morgan fingerprint calculation, including features for redundant environment handling and improved random SMILES generation. The contributions also involve adapting the code base to support atom feature extraction and incorporation of Coulomb Matrix calculations.
cheminformaticsrdkitpythonc-plus-plussources
thegodone/rdkit
Jun 2016 - Aug 2024
The official sources for the RDKit library
Contributions:17 PRs, 217 pushes, 54 branches in 8 years 3 months
rdkitinfinibandsystems-programmingrdmasources
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