Summary
Tibor Vansa is a data scientist and seasoned credit risk modeler with over a decade of experience applying probabilistic and econometric methods to banking portfolios. Based in Prague, he has led credit scorecard calibration, portfolio forecasting, and risk assessment for acquisitions while building and optimizing a department datamart to improve operational efficiency. Now at Datamole, he is transitioning his deep domain expertise toward broader machine learning and AI use cases, combining rigorous statistical training from Charles University and Humboldt-Universität with practical production modeling. Notably, his background spans both theoretical probability and hands-on implementation of customer-facing loan decision systems, giving him a rare blend of academic depth and business-focused impact.
8 years of coding experience
Econometrics, Information systems, Econometrics, Information systems at Humboldt-Universität zu Berlin
Master's Degree, Probability, Mathematical statistics and Econometry, Master's Degree, Probability, Mathematical statistics and Econometry at Charles University in Prague
Bc, General Mathematics, Bc, General Mathematics at Charles University
English, German, Czech