Zoltán Puha is a Senior Applied Scientist with nine years of experience, combining a PhD in Statistics with hands-on roles in experimentation, forecasting and agentic AI for data security. He has driven multi-month, staggered experiments and content-visibility forecasting at Zalando and now applies NLP and agentic approaches to (data) security at Uber. His research bridged Active Learning and Causal Inference, giving him a strong foundation for designing efficient, causally-aware experimentation and prediction systems. Comfortable with Python, R, SQL and production analytics, he moves fluidly between research and applied engineering to deliver measurable business impact. Based in Berlin with a cross-European academic background, he brings both rigorous statistical thinking and product-minded experimentation experience to complex ML problems.
9 years of coding experience
8 years of employment as a software developer
Master of Science (M.Sc.), Economics, Master of Science (M.Sc.), Economics at University of Amsterdam
Bachelor's degree, Sport Management, Bachelor's degree, Sport Management at Eötvös Loránd Tudományegyetem
Bachelor's degree, Applied Economics, Thesis: 5 Diploma:5 (out of 5), Bachelor's degree, Applied Economics, Thesis: 5 Diploma:5 (out of 5) at Eötvös Loránd University
Veres Pálné Secondary School
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at Tilburg University
code for Batch Mode Active Learning for Individual Treatment Effect Estimation
Contributions:16 commits, 2 PRs, 2 pushes in 7 months
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