Ivan Donin
DevOps Engineer at JoomPay
Moscow, Moscow, Russia
Summary
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RockstarIvan Donin is a DevOps engineer with 11 years' experience building and operating resilient infrastructure across large-scale e‑commerce and fintech environments in Moscow. He has driven cloud migrations, unified configuration and logging systems, and supported end-to-end launches at Yandex.Market and JoomPay, blending SRE rigor with hands-on DevOps delivery. Ivan contributes to the CatBoost open-source project, improving core computational routines and tutorial content, which reflects a rare combination of infrastructure expertise and applied machine-learning familiarity. Pragmatic and systems-focused, he’s comfortable writing non-critical production code, managing CI/CD pipelines, and troubleshooting both cloud and physical network stacks. An understated strength is his track record of integrating acquired companies’ systems—turning fragmented landscapes into manageable, centrally controlled platforms.
11 years of coding experience
3 years of employment as a software developer
Github Skills (22)
notebook10
catboost10
c-language10
learn-ruby-on-rails10
python10
user-manual10
machine-learning10
ipython10
basics10
gradient-boosting10
decision-tree10
jupyter-notebook10
cprogramming-language10
feature-selection9
algorithm9
Programming languages (6)
Github contributions (5)
catboost/tutorials
Oct 2019 - Mar 2022
CatBoost tutorials repository
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 1 comment in 2 years 5 months
Contributions summary:Ivan contributed to the CatBoost tutorials repository by adding and modifying tutorials related to machine learning tasks using the CatBoost library. Their commits involve integrating the MSRank dataset, developing tutorials for custom loss and metric, and feature selection techniques. The changes indicate a focus on demonstrating and expanding the capabilities of CatBoost for tasks such as regression and classification.
kagglepythoncatboostipython-notebookipython
catboost/catboost
Aug 2018 - Jan 2023
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Role in this project:Back-end Developer
Back-end DeveloperContributions:40 reviews, 275 commits, 306 comments in 4 years 6 months
Contributions summary:Ivan primarily contributed to fixing bugs and improving the functionality of the CatBoost library, focusing on the computational aspects. They addressed issues related to score calculation on the CPU, modifications to the Langevin noise implementation in the code, and improvements to tree plotting for non-symmetric trees. Furthermore, they modified code to include the functionality to handle empty leaves in the code and to accommodate feature tags.
kagglexgboostpythondata-mininglightgbm
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