Alexander Guschin
Academic Lead Of AI Olympiads (ex-Industry Lead Of AI Department) at Central University
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAlexander Guschin is an Industry Lead of AI with 11 years of experience building and shipping production ML across e-commerce, ride services and industrial manufacturing. He combines hands-on Python and MLOps expertise with leadership—running ML teams, designing production ML courses, and coaching competition-winning students (he coached the 2024 IOAI champion team). A Kaggle Grandmaster and co-author of Coursera’s popular “How to Win a Data Science Competition,” he translates competitive modeling techniques into robust, low-debt production systems and reproducible pipelines. He has contributed to open-source ML tooling at Iterative and to educational repos used by thousands of learners, and is known for automating grading and scalable coursework to amplify teaching impact.
11 years of coding experience
9 years of employment as a software developer
Master's degree, Data analysis, Master's degree, Data analysis at Moscow Institute of Physics and Technology (State University) (MIPT)
Русский, Английский
Github Skills (12)
scikit10
scikit-learn10
pandas10
jupyter-notebook10
machine-learning10
exploratory-data-analysis10
python10
data-science10
numpy10
eda10
data-analysis10
cross-validation9
Programming languages (6)
Github contributions (5)
vkantor/MIPT_Data_Mining_In_Action_2016
Oct 2016 - May 2017
"Data Mining in Action Course", Moscow Institute of Physics and Technologies
Role in this project:
Data Scientist
Data ScientistContributions:29 commits, 1 PR, 26 pushes in 6 months
Contributions summary:Alexander appears to be working on a data mining project related to the provided dataset, which likely pertains to a machine learning competition. The commit messages and code changes suggest the user is performing data loading, exploratory data analysis, and model training/evaluation. The user is implementing cross-validation techniques and utilizing popular Python libraries like Pandas, NumPy, and Scikit-learn to prepare and analyze the data, potentially training machine learning models to predict the target variable.
data-mining
hse-aml/competitive-data-science
Jun 2020 - Jun 2020
Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course
Role in this project:Data Scientist
Data ScientistContributions:8 commits, 7 PRs, 8 pushes in 1 day
Contributions summary:Alexander's contributions primarily involve integrating and working with data science libraries. The commits demonstrate the user's involvement in a project that is related to "How to Win a Data Science Competition" course, including modifying example code and importing of libraries. The user interacts with the core elements of the project, from data manipulation to machine learning model training.
data-science
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