Jan Ravnik
Field CTO at Ishango.ai
Brugg, Aargau, Switzerland
Summary
👤
Senior🎓
Top SchoolJan Ravnik is a Field CTO and scientist with 12 years of experience translating advanced physics and machine learning research into practical products and academic collaborations. He combines a PhD in condensed matter physics and an MBA with hands-on expertise in Python, SQL, parallel computing and IoT to lead MVP development, vendor selection, and cross-sector partnerships. Jan has built automated experimental platforms, contributed to top scientific journals, and taught university courses—receiving near-perfect teaching evaluations—so he excels at explaining complex ideas to diverse audiences. His work spans enterprise data engineering (entity resolution at Credit Suisse), curriculum development for DeepLearning.AI, and mentoring AI projects across academia and industry in Europe and Africa. Comfortable both at the lab bench and in client-facing technical strategy, he brings a rare mix of experimental rigor, production-oriented data skills, and startup-stage product delivery.
11 years of coding experience
6 years of employment as a software developer
Master of Business Administration - MBA, Master of Business Administration - MBA at Alliance Manchester Business SchoolDoctor of Philosophy - PhD Condensed Matter Physics, Doctor of Philosophy - PhD Condensed Matter Physics at University of Ljubljana, Faculty of Mathematics and Physics
Slovenian, German, English, Serbian
Github Skills (40)
javascript10
signal-processing10
data-mining10
topology10
machine-learning10
unstructured10
large-scale10
scale10
unstructured-data10
text-mining10
nodejs10
cpp10
rocket9
python9
browser-automation9
Programming languages (6)
Github contributions (5)
rupnikj/qminer
Jan 2015 - Jul 2019
Analytic platform for real-time large-scale streams containing structured and unstructured data.
Contributions:12 PRs, 769 pushes, 15 branches in 4 years 7 months
structuredanalyticstreamsmachine-learningscale
Qlector/qminer
Jun 2019 - Oct 2022
Analytic platform for real-time large-scale streams containing structured and unstructured data.
Contributions:11 PRs, 5 pushes, 5 comments in 3 years 4 months
structuredanalyticstreamsmachine-learningscale
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