О себе
Key responsibilities at my last job: - ML models development: participated in developing application, behavioral, fraud, and collection scoring models for new and repeat customers in the PDL and installment loan segments. Specifically, I performed feature engineering for the scoring cards by creating new factors and optimized the information value of factors by building small intermediate logistic regression models on insignificant sets of factors (or through other transformations, such as pairwise factor multiplication or a genetic algorithm). I implemented the replacement of XGBoost with LightGBM and CatBoost in the models, which increased the Gini coefficient by 8–10% (in some models, up to 20%). Used: Python, R, PostgreSQL, MySQL. - Monitoring feature stability to detect anomalies: calculated thresholds for monitoring changes in the stability of the information value and WoE of each factor to preemptively retrain models before the Gini coefficient decreased. Used: Python, Airflow, SQL, MS Power BI. - Daily operations: wrote ad hoc SQL scripts for risk analytics, validated WSDL files, and supported reporting and dashboards (Tableau and Power BI). Technical Skills: Languages: Python, R, SQL Developer Tools: Git, Docker Databases: MySQL, PostgreSQL, Google BigQuery Data Visualisation: Tableau, MS Power BI Kazakhstan residence permit Career gap - parental leave
Опыт работы
4finance Ltd (SMSfinance/Vivus brands)
Analyst
Designed and implemented an automated monitoring system for scorecard features to detect characteristic drift over time (e.g., declining IV, unstable borrower segmentation) Enhanced ML model feature selection by applying linear (regression-based) and non-linear (genetic algorithms) methods to boost the predictive power of low-information value features. Developed higher-performance credit scorecards (PDL/installment products) by migrating from XGBoost to LightGBM and CatBoost Optimized fraud detection models that increased fraudster identification by Performed WSDL files validation and approval for credit policy updates
- Optimized fraud detection models that increased fraudster identification
Movavi.com
Data analyst
Developed predictive models for conversion/profit rate forecasting (ARIMA/SARIMA) Designed and ran A/B tests Automated reporting (Tableau) Implemented end-to-end analytics using Funnel-Based Attribution Designed key metrics for user behavior tracking (web/mobile) Established data collection pipelines (BigQuery, Google Analytics, GTM) Created technical specs for OLAP cube-based reporting systems
Inversion Sensor Ltd
Engineer
Assembled and tested fiber-optic part of fire alarm unit Developed temperature compensation methods for fiber-optic strain sensors Calibrated fiber-optic sensors and analyzed sensor data using Python/MATLAB
Образование
Moscow State University
Faculty of Artificial Intelligence, Data Science (AI Masters MSU)
МагистрNovosibirsk State University
Department of Physics, Master of Science in Quantum and Optical Electronics
МагистрNovosibirsk State Technical University
Department of Physical Engineering, Bachelor of Science in Laser and Optical Engineering
БакалаврКурсы
EPIC Institute of Technology - annual program at EPAM (Time Series Analysis)
EPIC Institute of Technology
Agents Week, Yandex School of Data Analysis
Yandex School of Data Analysis
Full Cycle ETL Engineer program for IT specialists at Neoflex
Neoflex