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Andrei Shelopugin

@andreishelopugin

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Machine Learning Engineer at AIStats, developing real-time football models and player evaluation systems.

Russian Federation

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What I'm looking for

I'm seeking senior machine learning and research roles working with event, tracking, and skeletal data, real-time systems, and player evaluation.

At AIStats, I established the Data Science department and developed football models for team and player evaluation, including xG, xP, EPV, and Glicko-2-based club ratings. I also deployed low-latency systems for real-time metric prediction across live matches.

At Tennisi.bet, I developed sports prediction models, with over 50 profitable models in production when I left, and real-time football models using Markov chains and recurrent neural networks. My research includes football ratings, player abilities, and forecasting player intentions from event and tracking data.

Experience

Work history, roles, and key accomplishments

Data Science Team Lead

AIStats

Aug 2024 - Jun 2026 (1 year 10 months)

Established the Data Science department, managed a team of two, and defined a 2-year roadmap for football analytics. Built and deployed machine learning models for team and player evaluation, including xG, xP, EPV, and Glicko-2-based club ratings, and developed low-latency real-time prediction systems.

Machine Learning Gradient Boosting Transformers XP Practices Expected Possession Value Glicko 2 Real Time Systems Team Management Roadmapping

Head of Data Science

Tennisi.bet

Nov 2019 - Nov 2023 (4 years)

Designed and deployed machine learning models for sports statistical metrics prediction, achieving over 50 profitable models in production. Developed real-time football match models using Markov chains and recurrent neural networks, and reimplemented legacy pipelines from R to Python with a 4x speedup.

Machine Learning Gradient Boosting Markov Models Recurrent Neural Networks Python R Project Management Team Leadership Code Review

Data Scientist

Iceberg Sports Analytics

Jul 2018 - Feb 2020 (1 year 7 months)

Analyzed hockey zone entries and exits and classified player duels using event and tracking data. Built descriptive ML models characterizing team and player performance and developed models describing complex hockey match dynamics.

Machine Learning Python Sports Analytics Event Data Data Tracking