End-to-end development and deployment of ML solutions for the banking sector, including problem formulation, data analysis, ETL, feature engineering, model validation, and results interpretation. Automation of ML workflows. Addressing ML challenges to enhance retail banking operations through geospatial data analysis. Developing AI solutions for recruitment and personnel management using language models.Accomplishments:- Developed and successfully implemented a resume-matching system for corporate HR platforms, enhancing candidate selection efficiency- Designed a predictive HR analytics system to forecast employee burnout and attrition, improving workforce management strategies.- Created and implemented a geospatial data-driven solution for optimising ATM placement, resulting in a quarterly savings of over 1 billion rubles.Stack: Python (Pandas, Sklearn, Pytorch, MatplotLib, CatBoost), SQL (Teradata, Hadoop, Hive, Impala). Spark, CI/CD, Kubernetes, Docker, Git, Jira, Confluence.

Leading Data Science team (6 DS and 2 DE). Construction of quantitative models for predicting price movements for stocks and derivatives. Creating an internal product for making trading decisions based on machine learning algorithms using fundamental and technical data about assets. Development and implementation of a risk management system.Accomplishments:- Conceived, developed, and implemented a predictive analytics system for analysing and forecasting stock price movements for over 15,000 global companies, resulting in a 30% annual profit increase for the fund.

Development and implementation of price forecasting models for various asset classes (cryptocurrencies, commodities, stocks, derivatives). Building predictive analytics systems based on machine learning (classification, regression, anomaly search) for trading operations. Formation and verification of hypotheses. Analysis of the effectiveness of CRM campaigns. Conducting analytical research for working with clients, marketing and financial management.

Support for investment decision-making in terms of creating and updating internal databases of instruments available for investment. Creation of predictive financial models of companies – issuers of stocks and bonds (profit/loss, balance of assets/liabilities, cash flow). Development and support of fair value measurement models for various financial instruments, the formation of analytical notes and professional judgments based on the results of such an assessment.