Viktor Kunovski
Principal Consultant, ML, Data Science & AI
Randstad, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolViktor Kunovski is a Principal Consultant in ML, Data Science & AI based in the Randstad with over a decade of hands-on experience bringing machine learning systems from prototype to production. A former mechatronics engineer, he blends robotics and embedded-systems intuition with modern ML practices to deliver solutions in recommender systems, object detection, LLMs, semantic search and information extraction. At Reaktor he has led projects as tech lead, team lead and project lead, shaping proposals, client relationships and technical strategy for medium and large enterprises. He contributes to practical open-source work—improving a SAR recommender implementation and evaluation in the recommenders-team repo—showing attention to metrics, normalization and test correctness. Comfortable across Python, C++, web stacks and cloud data engineering, he’s equally at home architecting systems and mentoring teams to grow their ML capabilities.
10 years of coding experience
11 years of employment as a software developer
M.Sc. in Computer Science, Computer Networks and Distributed Systems, M.Sc. in Computer Science, Computer Networks and Distributed Systems at University of AntwerpB.Sc. in Mechanical Engineering, Mechatronics, Robotics, and Automation Engineering, B.Sc. in Mechanical Engineering, Mechatronics, Robotics, and Automation Engineering at Faculty of Mechanical Engineering at Ss.Cyril and Methodius University of Skopje
English, Macedonian, Dutch
Github Skills (11)
scikit-learn10
pandas10
jupyter-notebook10
machine-learning10
recommendation-system10
python10
data-science10
scikit10
ranking9
ai9
user-manual8
Programming languages (6)
Github contributions (5)
recommenders-team/recommenders
Jun 2020 - Aug 2020
Best Practices on Recommendation Systems
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 1 PR, 11 comments in 1 month
Contributions summary:Viktor primarily contributed to the development and evaluation of a SAR (Similarity-based Recommendation) model within a Jupyter Notebook environment. Their work involved adding and verifying the inclusion of accuracy metrics such as R-squared and explained variance to assess model performance. Moreover, the user corrected normalization parameters within the SAR implementation and improved associated tests to reflect those corrections, ensuring the model's predictive behavior aligned with intended results. The user also made changes to a notebook in the examples folder of the repository that demonstrated the usage of the SAR model.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
Intermodalics/ros3djs
Jan 2016 - May 2019
3D Visualization Library for use with the ROS JavaScript Libraries
Contributions:1 PR, 41 pushes, 7 branches in 3 years 4 months
visualization-library3d-visualizationjavascriptrosjavascript-libraries
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