Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 2


OutHouse:

Telecom with the commercial bank launched a Fintech start-up. The core of it was to build credit scoring based on machine learning algorithms, enriching bank's scoring with telecom scoring. Besides that, the trigger mechanism was developed to find consumers more likely to take a loan. 

Achieved  40% Gini of credit scoring model, resulting in earning of 6 million soms : 
           - Change old fashion credit scoring model to the machine learning model, using ensemble algorithms(Stacking)
           - Generated new features based on banking data with help of risk specialists
           - Deployed credit scoring model into the banking system (Colvir) 
           - Built telecom scoring model and calibrated it to the distribution of bank's target
           - Integrated telecom scoring into bank credit scoring model, which raised Gini by 8 percent
           - Developed a trigger mechanism by geolocation and action trigger points. In addition to the propensity model, defining the tendency of people to loans.
           - Deployed A/B test pipeline

InHouse:

Developed churn model + uplift models achieving 4 % decline in churn-out rate
Created consumer segmentation model using clusterization
Constructed machine learning orchestration using Airflow to manage running workflows of data uploading(DAGs) and generating predictions
Created web interfaces for retrieval of ML models predictions, which automated hours of manual work 
Developed a dashboard of models predictions statistics to monitor models' stability