A part-time position, working Saturdays only. Teaching experience: Data analysis: Statistics and probability theory (R, Python, Excel), Decision-making & Game Theory (Excel, R) with English instruction for foreign students.

Spearheaded the end-to-end development and deployment of a predictive model that forecasts equipment failure probability, leveraging diagnostic sensor data. Engineered the technical specifications and led the cross-functional execution for the model's design, pilot, and full-scale implementation. Quantified physical risks—including climate impact and emergency scenarios—into a actionable risk matrix, enabling proactive maintenance scheduling and reducing unplanned downtime.

- ML model for KPI evaluation in Pharma consuming, 2022 Conducted research using quantitative techniques, such as statistical modeling and system identification techniques, to derive actionable insights for clients - Maintenance of railway infrastructure, 2019-2021 Developed algorithms for monitoring railways condition that took into account both historical data from existing systems and future predicted information. - Maintenance of oil and gas pipelines (time-series analysis and forecasting), 2017-2018 Identified issues with oil & gas pipelines using time-series analysis that improved reliability and accuracy. - Patenting, Publishing Q1 - Q4 papers, Contracting & Expertise for the Russian Federation List of Experts in Automated Tech: - Applied deep engineering knowledge across multiple disciplines related to automation technologies including computer vision, deep learning algorithms development Artificial Intelligence (AI). - Wrote technical content around researching new trends in AI technologies based upon publications with IEEE.

- Delivering seminars introducing cutting-edge topics within the field of data science and neuromarketing. - Editing and publishing scientific articles for submission to technical journals.

Developing and presenting proposals, technical specifications, and outlays of the DS projects Stack: Numpy, Pandas, Sklearn, SciPy, XGBoost, CatBoost, Scikit-learn, Prophet; Colab, GitHub

In 2018, 2019, 2021, Lectured ML-courses Python, Google Colab, Datacamp.com

Provided a ML-course using Google Colab and the Datacamp.com platform.

Supervised 3 global IT projects for subarctic oil pipelines to predict and locate heat losses, utilizing ML-models, a PostgreSQL database, client-server architecture.

- Experienced in teaching Optimization Methods, Systems Architecture, and Object-Oriented Programming. - Supervised Master's degree diplomas related to software development, with support from the Russian Foundation for Basic Research. - Winner of 2 Regional Scientific Awards in 2009 and 2012.

- Led the automation of data collection, processing, and analysis to uncover new insights into the root causes of metal degradation and defects - Developed custom software algorithms to identify patterns, trends, and changes in multiple sets of data - Wrote patents and publications which documented results of collaborative research investigations - Implemented advanced statistical techniques to improve accuracy and efficiency of defective parts detection

Teaching Experience: - User-Oriented Design - Introduction to Computer Science Promoted Platform at Universities & Conferences

Organized technical seminars and workshops for college students on multicore architectures and related topics Provided mentorship to junior colleagues as part of Intel’s ‘Scaling Up’ program Wrote articles for the Intel Software College Wrote the manual ‘Scalable Software Design for Multicore Architectures’ for the course ‘Multicore: Theory and Practice’ (in Russian)