Sergey Skvortsov
Development Team Lead at Yandex
Moscow, Moscow, Russia
Summary
🤩
Rockstar🎓
Top SchoolSergey Skvortsov is a Development Team Lead at Yandex with a decade of experience building high-performance, low-latency backend systems and distributed storage for search infrastructure. He has designed and optimized on-disk erasure-coded key-value stores, built a highly efficient code search service used by thousands of developers, and helped architect a universal streaming data pipeline. A pragmatic C++ engineer and former lecturer in C++, Sergey contributes to major open-source projects like CatBoost, where he focused on low-level performance, safety around threading and fork, and function-ref utilities that improve core library robustness. He combines deep systems-level expertise with hands-on leadership, routinely turning complex performance constraints into maintainable production solutions.
10 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Higher School of EconomicsAESC MSU — Kolmogorov Boarding SchoolComputer Science, Computer Science at Yandex School of Data Analysis
Github Skills (16)
machine-learning10
c-language10
cprogramming-language10
multithreading10
gradient-boosting10
optimization9
decision-tree9
algorithms8
algorithm8
gpu7
gpgpu7
accelerated-computing7
javas6
python6
r6
Programming languages (8)
Github contributions (5)
catboost/catboost
Oct 2019 - 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:35 commits in 3 years 3 months
Contributions summary:Sergey primarily focused on enhancing the `catboost` library's functionality through low-level code changes. Their contributions involved adding specializations for const/ref qualified member functions and creating a lightweight function reference wrapper, impacting the library's efficiency and flexibility. The user also addressed potential issues related to multi-threading, specifically preventing deadlocks after fork operations within thread pools. These modifications suggest a focus on improving the core performance and stability of the CatBoost library.
kagglexgboostpythondata-mininglightgbm
dc-lab/sky
Dec 2019 - Oct 2020
Contributions:43 PRs, 40 pushes, 44 branches in 10 months
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