Jeroen Overschie is a Machine Learning Engineer with 15 years of software experience, currently helping enterprises scale MLOps and adopt generative AI at Xebia Data from Amsterdam. He builds production-grade ML pipelines across Google Cloud, Azure and AWS, and has delivered projects for clients such as Vattenfall, ING, AXA and Signify, often combining deployment work with hands-on model development. Jeroen teaches and mentors industry teams, having led multiple multi-day trainings for organizations like Rabobank and Bol.com, and is a regular speaker at PyData and PyCon. His background includes applied MLOps for high-volume finance and energy domains—deploying AML detection on Spark and predictive-maintenance models for wind parks—showing a knack for operationalizing complex models. He holds an MSc in Data Science and Systems Complexity (cum laude) from the University of Groningen and pairs academic rigor with practical consulting and presales experience. Colleagues describe him as someone who bridges research-grade techniques and production realities, often surfacing non-obvious trade-offs between model complexity and long-term maintainability.
15 years of coding experience
9 years of employment as a software developer
High School Diploma N&T (Math/Physics/Chemistry/Computer Science), High School Diploma N&T (Math/Physics/Chemistry/Computer Science) at Arentheem College Thomas a Kempis
Master of Science - MS Data Science and Systems Complexity | Computing Science, Master of Science - MS Data Science and Systems Complexity | Computing Science at University of Groningen
Bachelor's degree Mixed fields, Bachelor's degree Mixed fields at Humboldt University of Berlin
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