Geoffrey Harrazi is a senior MLOps Data Engineer Tech Lead based in Paris with a decade of experience building production ML platforms and data pipelines for luxury brands. He currently leads ML infrastructure at Parfums Christian Dior, delivering scalable MLOps practices on Google Cloud, Kubernetes and Dataiku to support model development across the group. Previously at LVMH and Ysance, he industrialized data management and model production pipelines (Vertex AI, Kubeflow, CI/CD, Continuous Training) to accelerate marketing analytics and experimentation. His background spans rich cross-disciplinary training—MIAGE and Specialized Master in Big Data from Sorbonne, plus studies in Sweden and France—highlighting a global, impact-focused approach to data science. A passionate data science enthusiast, he consistently turns complex data value chains into auditable, scalable solutions that drive business outcomes.
10 years of coding experience
4 years of employment as a software developer
Single Course: Machine Learning & Mathematics for microdata analysis, Informatique, Single Course: Machine Learning & Mathematics for microdata analysis, Informatique at Blekinge Tekniska Högskola
Career Track, Data Scientist with Python, Informatique, Career Track, Data Scientist with Python, Informatique at DataCamp
MIAGE, Ingénierie informatique, Système d'information, MIAGE, Ingénierie informatique, Système d'information at Université Paris 1 Panthéon-Sorbonne
BTS SIO, Services Informatiques aux Organisations, BTS SIO, Services Informatiques aux Organisations at Lycée Jean-Jaques Rousseau
Computer and Systems Sciences, Data mining, Data Warehousing, Computer and Systems Sciences, Data mining, Data Warehousing at Stockholms universitet
Specialized Masters® Big Data: Advanced Analytics for Decision Making, Data Science, Specialized Masters® Big Data: Advanced Analytics for Decision Making, Data Science at Université de Technologie de Troyes
Baccalauréat, STI - Sciences et Technologies Industrielles, Mention Bien, Baccalauréat, STI - Sciences et Technologies Industrielles, Mention Bien at Lycée Jean-Jaurès
Experimenting MLOPS with CML and DVC for the kaggle BIke sharing prediction problem.
Contributions:1 review, 14 PRs, 32 pushes in 3 days
kagglebike-sharingexperimentingpredictioncml
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.