Razmik Melikbekyan
Data Science Manager
Krakow, Lesser Poland Voivodeship
Summary
👤
Senior🎓
Top SchoolRazmik Melikbekyan is a Data Science Manager based in Krakow with nine years of experience building and leading ML teams that deliver production-grade solutions in computer vision, geospatial analytics, and traditional machine learning. He pairs a PhD-level mathematical background with strong programming and MLOps skills, enabling smooth model deployment and measurable business impact across enterprises like Philip Morris and Agri-tech leader Intelinair. Known for cross-functional collaboration and negotiation, he has advanced team delivery from hands-on model development to operationalized pipelines. An active contributor to ML tooling—improving code quality and feature creation in the notable AutoGluon project—he brings a pragmatic eye for maintainability and reproducible workflows.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Mathematics, Doctor of Philosophy - PhD Mathematics at Yerevan State UniversityMachine Learning, Machine Learning at Armenian Code Academy
Armenian, Russian, English
Stackoverflow
748reputation
38kreached
47answers
2questions
Github Skills (17)
python10
machine-learning10
datatable10
datatables10
tabular10
automated-machine-learning10
ensemble-learning9
deep-learning8
deeplearning-ai8
scikit-learn7
scikit7
knn6
h2o6
gridsearchcv6
pandas6
Programming languages (2)
Github contributions (5)
autogluon/autogluon
Feb 2020 - Apr 2020
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 27 PRs, 44 comments in 2 months
Contributions summary:Razmik primarily focused on improving the codebase's maintainability and functionality within the AutoGluon project. Their contributions included type checking and indentation corrections across multiple files, indicating a focus on code quality and readability. They also addressed basic pep8 corrections and bug fixes, particularly in string formatting, suggesting a commitment to code standards and error resolution within the machine learning framework. Furthermore, the user worked on optimizations and improved the feature creation process.
forecastingimage-textmlppythonmeta-learning
intelinair/yield_forecasting
May 2021 - Aug 2021
Contributions:62 reviews, 59 PRs, 83 pushes in 3 months
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