Nicolas Oulianov is a data scientist and founding engineer with six years of experience turning ML research into production-ready systems across startups and consulting firms. He has built end-to-end products—from a mobile carpooling app and backend services to deployment pipelines for transformer models—combining hands-on software engineering with applied machine learning. His background spans entrepreneurship (Ynstant, phospho), strategy-led analytics at QuantumBlack/McKinsey, and research internships at Criteo and Foundamental, reflecting strength in contextual advertising, NLP, and GenAI. Educated at Télécom Paris, Institut Polytechnique and HEC Paris, he pairs rigorous technical depth with business sensibility and has shipped features that measurably improved metrics (e.g., boosted contextual ad visits threefold in offline tests). Based in Paris with international experience in Berlin and San Francisco, he seeks to accelerate adoption of cutting-edge AI in real products while remaining an active practitioner (see github.com/oulianov).
6 years of coding experience
6 years of employment as a software developer
French Scientific General Baccalaureate (Mathematics specialty), French Scientific General Baccalaureate (Mathematics specialty) at Lycée Henri IV
Prépa class, Mathematics, History, Geography, Literature, Philosophy, Languages, Prépa class, Mathematics, History, Geography, Literature, Philosophy, Languages at Lycée Janson-de-Sailly
Master of Engineering - MEng, Artificial Intelligence, Master of Engineering - MEng, Artificial Intelligence at Télécom Paris
Licence, Applied Mathematics, Licence, Applied Mathematics at Paris-Sud University (Paris XI)
Séminaire leadership et esprit d'équipe, Séminaire leadership et esprit d'équipe at Académie militaire de Saint-Cyr Coëtquidan
Master's degree, Digital, Master's degree, Digital at HEC Paris
Master 2 (M2), Data Science, Master 2 (M2), Data Science at Institut Polytechnique de Paris
Control AI robots. Community-driven UI middleware for controlling robots, recording datasets, training action models. Compatible with SO-100 and SO-101
Contributions:98 releases, 68 reviews, 154 PRs in 9 months
aimiddlewareroboticsvlaso100
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