Founder Bang & Olufsen Distribution In Slovenia at Pikado d.o.o.
Slovenia
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Summary
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Rockstar
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Top School
Tomaž Hočevar is an entrepreneurial engineer with 13+ years of experience building and running consumer electronics and luxury AV businesses in Slovenia, currently co‑founder and head of finance, marketing and operations for Bang & Olufsen distribution in the country. He combines hands‑on technical training (mechanical engineering) with a Master’s in Management and deep commercial experience across retail scale‑ups, logistics and systems implementation. A natural problem solver and out‑of‑the‑box thinker, he has led rapid revenue and headcount growth initiatives and implemented advanced information systems during periods of heavy expansion. In parallel he contributes to open‑source data science (Orange3) and teaches at the University of Ljubljana, bridging practical business leadership with algorithmic and machine learning interests. Multilingual and internationally mobile, he brings a broad, cross‑functional perspective useful for product, technical and strategic decisions.
13 years of coding experience
12 years of employment as a software developer
Master’s Degree, Management and Entrepreneurship, Graduated, Master’s Degree, Management and Entrepreneurship, Graduated at University of Ljubljana, Faculty of Economics
Mechanical engineer, University degree, Heat generation under dynamical stress, Mechanical engineer, University degree, Heat generation under dynamical stress at Faculty for Mecanical Engineering, University of Ljubljana
English, German, Croatian, Serbian, Bosnian, Slovenian
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Role in this project:
Data Scientist
Contributions:1 review, 257 commits, 134 PRs in 10 years 4 months
Contributions summary:Tomaž contributed to bug fixes, feature implementations, and tests for machine learning algorithms within the Orange3 data mining framework. Their work focused on classification, particularly addressing issues with sparse data, single instances, and probability estimation within the model. They also implemented a Naive Bayes model using a storage contingency table and added several tests for the sklearn tree and naive bayes implementations.
Contributions:10 pushes, 5 branches in 3 years 4 months
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