Oscar Hernandez is a Data Engineer/Analyst based in Berlin with a decade of experience applying statistical rigor and scalable engineering to product and scientific problems. He combines a PhD-level background in theoretical and computational nuclear physics with hands-on expertise in AWS, PySpark, Bayesian and frequentist A/B testing, and machine learning to drive metrics-informed product decisions at Gen (formerly Avira). His work spans building ETL pipelines and Tableau dashboards for large-scale marketing experiments to developing regression and localization models for industrial applications, reflecting a rare mix of research-grade uncertainty quantification and production data engineering. Notably, he has translated advanced techniques like MCMC, maximum-entropy reconstruction, and physical-model–informed clustering into practical solutions across industry and academia.
10 years of coding experience
10 years of employment as a software developer
Masters in Physics Theoretical and computational nuclear physics, Masters in Physics Theoretical and computational nuclear physics at University of Manitoba
Doctor of Philosophy (PhD) Theoretical and computational nuclear physics, Doctor of Philosophy (PhD) Theoretical and computational nuclear physics at The University of British Columbia
Contributions:105 pushes, 1 branch in 3 years 9 months
github-io-pagejsshieldsiojekyll
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