Andrej Čopar
Senior DevOps Engineer at Genialis
Slovenia
Summary
🤩
Rockstar🎓
Top SchoolAndrej Čopar is a Senior DevOps Engineer based in Slovenia with 11 years of experience building and maintaining cloud-native infrastructure and deployment pipelines. After a PhD path at the University of Ljubljana, he transitioned into DevOps roles at Genialis where he progressed from engineer to senior engineer, focusing on reliability, automation, and scalable ML/biology-focused platforms. He blends strong research instincts with practical engineering—evident from contributions to the Orange3 data-analysis project where he implemented SGD regression features and evaluation improvements for ML workflows. Comfortable working at the intersection of research and production, he’s adept at translating experimental models into reproducible, tested pipelines. Colleagues value his methodical approach and the rare combination of academic depth and hands-on deployment craft.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Master of Engineering - MEng, Bachelor's Degree, Computer and Information Sciences, Doctor of Philosophy - PhD, Master of Engineering - MEng, Bachelor's Degree, Computer and Information Sciences at University of Ljubljana
Github Skills (16)
scikit10
data-mining10
regression10
machine-learning10
orange10
python10
data-science10
scikit-learn10
classification9
data-visualisation9
data-visualization9
data-visualizations9
testing8
numpy8
auc7
Programming languages (7)
Github contributions (5)
biolab/orange3
Nov 2014 - Sep 2019
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Role in this project:
Data Scientist
Data ScientistContributions:45 commits, 26 PRs, 5 pushes in 4 years 10 months
Contributions summary:Andrej primarily focused on developing and testing regression models within the Orange3 data analysis framework. Their contributions included adding a Stochastic Gradient Descent (SGD) Regression widget, incorporating the `sklearn` library and associated features (e.g., loss functions, penalty types, learning rates), and writing tests for the new functionality. The user also improved the evaluation of the learners by implementing AUC metrics and refining the multiclass handling.
data-analysisdata-miningdata-sciencemachine-learningdata-visualization
acopar/crow
Mar 2017 - Sep 2018
Scalable multi-GPU approach to non-negative matrix tri-factorization.
Contributions:96 commits, 75 PRs, 74 pushes in 1 year 6 months
multi-gpu
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