Julia Kotova is a data scientist and Python developer with 14 years of hands-on experience building ML/DL solutions and designing code architectures, currently developing an AutoML platform at SK-SKHD in collaboration with Bauman MSTU. She has recent production experience with Spark on Yandex Data Proc for large-scale data preparation and modeling, and has implemented computer vision (YOLOv5), time-series forecasting, OCR and predictive analytics across industrial use cases. Fluent in object-oriented design and design patterns, she pairs practical engineering (Flask, Docker, Kafka, TensorFlow/Keras, XGBoost) with strong data tooling (Pandas, Statsmodels, SQL) to move models into service. Her open-source contributions include front-end work on the popular Cube.js repository, creating interactive Mapbox and Highcharts visualizations that bridge analytics and UX. A continuous learner, she completed Yandex’s Data Science and Data Engineer programs and cites Fluent Python and Head First Design Patterns among her influences. Based in Moscow, she balances technical rigor with outdoors pursuits—swimming, hiking and cross-country skiing—which she credits for sustained focus on long-term projects.
14 years of coding experience
6 years of employment as a software developer
Software engineer, Computer and automated systems software development, Software engineer, Computer and automated systems software development at Siberian State University of Telecommunications and Information Sciences
📊 Cube — Universal semantic layer platform for AI, BI, spreadsheets, and embedded analytics
Role in this project:
Front-end Developer
Contributions:7 commits, 8 PRs, 51 pushes in 3 months
Contributions summary:Julia primarily contributed to the front-end examples within the cube-js/cube repository. They focused on implementing and styling various data visualizations, particularly using libraries like Mapbox and Highcharts. Their work included creating interactive dashboards and integrating them into the example applications. The user also made minor improvements and fixes to existing code.
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