Henrik Kjeldsberg is a seasoned data scientist and engineer with 11 years of experience building data platforms, ML models, and scalable backend systems across finance, research, and energy sectors. He combines deep academic training in computational fluid mechanics and a PhD in computational physiology with hands-on expertise in Python, SQL, ETL, FastAPI, cloud-native deployments (Azure, Kubernetes) and Kafka-driven pipelines. Henrik has moved between research-grade simulation work and production engineering—automating ETL and deploying ML-driven APIs for energy forecasting and fund performance analytics. He advises at board level on investment selection and portfolio oversight, bringing a technical lens to strategic decisions. Notably, he has architected end-to-end solutions spanning React frontends to Kotlin microservices and optimized parallel simulations on HPC/cloud, reflecting a rare blend of applied research and production delivery. Based in Oslo, he excels at turning complex numerical models and messy data into actionable, automated insights.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Fluid Mechanics, Bachelor of Science - BS, Fluid Mechanics at Universitetet i Oslo (UiO)
Doctor of Philosophy - PhD, Computational Physiology, Passed, Doctor of Philosophy - PhD, Computational Physiology, Passed at University of Oslo
Contributions:5 commits, 1 push, 1 branch in 1 day
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