Instructors

Instructors

All three instructors work as data scientists in the experimentation and causal inference team at Vinted, Europe’s largest second-hand online marketplace.

The team serves as a center of excellence in developing and applying causal inference and machine learning techniques at scale and developing internal educational material to upscale 100+ data scientists. The experimentation and causal inference team actively collaborates with researchers at Stanford University, the University of Amsterdam, and Cornell University.

All three instructors have past academic research experience, as well as lecturing experience at the University of Washington (USA), University of Warwick (UK), Vilnius University (LT), and ISM University (LT).

• Agnė Reklaitė - a seasoned data scientist with a Ph.D. in Applied Statistics from Vilnius University, her thesis was on the dynamic hierarchical factor models. She has 10+ years of tenure in the industry and is currently developing an internal experimentation platform at Vinted; she has extensive experience in data analysis, management of analytical projects, and the implementation of statistical and econometric methods.
• Giedrius Blazys - a University of Washington alumnus with a Ph.D. in Applied Econometrics, with 18 years tenure in industry and academia. After a research appointment in Labor Economics at Uppsala University, Giedrius advanced to Euromonitor International, crafting econometric models for demand forecasting in various industries, and later expanded his expertise in applying machine learning across various sectors. Currently, as a Data Scientist in experimentation and causal inference team at Vinted, Giedrius specializes in developing models for large-scale experiment analysis, aiding both his team and decision-makers across the company.
• Jevgenij Gamper - is a Staff Data Scientist at Vinted; his role involves leadership across all experimentation and causal inference aspects. Previously, he led a machine learning team that developed and deployed models all across the company. Before, Jevgenij worked in industry and academia on applying machine learning and statistical techniques to climate data, remote sensing, astronomy, and medical imaging. While pursuing his Ph.D. at the University of Warwick, Jevgenij published at top venues such as CVPR. Furthermore, his academic work has been licensed to large pharmaceutical and technology companies.