Those who accompany you in your transformation

Data Engineering and Cloud expert, Romain has more than 11 years of experience, including several years as Lead Developer on high-performance Smart Building solutions. He designed and shipped processing engines able to absorb hundreds of thousands of sensor data points per minute, as well as clustered databases handling more than 10 million dynamic records. Microsoft Azure DevOps Engineer Expert certified, he masters back-end development (Python, C#), DevOps (Docker, Kubernetes, Terraform) and LLM agents. As a trainer in Python, cloud, DevOps and applied generative AI, he trains with one obsession: helping every learner design and deploy architectures that are truly scalable in production.

Apache Kafka expert and Senior DevOps Engineer, Florian has more than 10 years of experience industrializing real-time streaming platforms for major French accounts — Crédit Agricole, AXA, Colissimo, Unibail. At Crédit Agricole, he co-built and operated one of the largest Kafka platforms in France: a Topic-as-a-Service offering covering about fifty clusters, more than 100,000 topics and nearly 1.5 million events per second in production. ESIEA graduate, Confluent event speaker and contributor to the open-source Kafka-Python library, he masters the full data-in-motion ecosystem: Kafka, Kafka Connect, Spark Streaming, Ansible, Terraform, Kubernetes and observability (Prometheus, Grafana). As a trainer at DataScientist, he teaches production engineering standards: design reliable, secure and truly scalable streaming pipelines on enterprise use cases.

Data Engineering expert and software engineer graduated from Polytech Lyon, Valentin has more than 10 years of experience at the intersection of software, real-time data and product. He built and shipped demanding SaaS platforms: at Hxperience on Smart Building, then in New York at Vero, where as Lead Tech then product lead he drove rental screening tools and supported a Series B raise. Today Lead Product & Engineering, he masters Python/Django, REST APIs, microservices architectures and software craftsmanship (automated tests, CI, code review). A key instructor on the Modern Data Engineer Master's (RNCP level 7), he trains learners to design architectures and code you would confidently ship to production.

Full-stack developer graduated from INSA Centre Val de Loire, Dung has more than 5 years of experience with Next.js, React.js and JavaScript. As architect of the DataScientist.fr learning experience, he designs, evolves and maintains in production our learning platform used daily by learners: fast, accessible and polished interfaces, smooth journeys, immediate feedback and built-in live coding. His obsession is UX/UI in service of pedagogical performance — removing every friction between the learner and the targeted skill. He turns the pedagogical ambition of the expert team into a reliable, enjoyable product, from the first click to certification.

Data Engineering expert and Talend specialist with more than 12 years of experience, Anass has led critical data projects in production for major industrial and healthcare accounts. At Michelin, he migrated more than 250 Talend routes to Azure with real-time monitoring; at Almerys, he contributed to redesigning a system processing more than one billion rows per month for 12 million insured people; at Limagrain, he led a Talend 7.3 → 8 migration that significantly cut infrastructure costs. He masters Talend ETL/ESB, real-time monitoring (Grafana, Prometheus, ELK), cloud, DevOps and optimized SQL. A pragmatic trainer, he anchors learning in high-stakes real cases: industrializing reliable pipelines is how you become a true data engineer.

Machine Learning Engineer graduated from Université de Technologie de Compiègne (UTC), Cristian has more than 6 years dedicated to Big Data and machine learning in production. Across his missions, he designed and industrialized distributed processing pipelines and models deployed at scale — where a simple notebook is no longer enough. Expert in Python, PySpark, Spark and Scala, comfortable on cloud environments and Microsoft Fabric, he knows precisely what separates a prototype from a robust system running on a cluster. Recognized for his pragmatism as a trainer, he has learners work on real cases and massive volumes rather than simplistic examples, so they can ship machine learning that holds in production.