Job Responsibilities:

• Analyze credit history data, prepare, clean, and model in Python Jupiter notebook environment – for scoring models.
• Analyze data (DWH, BigQuery) on various aspects of client statistics: defaults, conversion, contract terminations, targeted marketing campaigns, behavioral analytics, client base dynamics, segmentation, including special requests from management.
• Identify patterns and trends.
• Develop predictive models for different aspects of banking activities.
• Prepare decisions and recommendations based on analysis results.
• Analyze test results and provide recommendations for credit policy, strategy, and marketing campaigns.
• Prepare reports for management (including ad-hoc reports upon request).

Candidate Expectations:

• Solid foundation: linear algebra, probability theory, mathematical analysis, mathematical statistics.

• At least 1 year of experience in ML/DS.

• Proven stack through training and pet projects (GitHub required).

Skills You Have:

• .ipynb: LightGBM, XGBoost, CatBoost, Sklearn, Matplotlib

• .py: Python Core (OOP), FastAPI, Airflow

• .sql: PostgreSQL, MS SQL

• .jpeg: Tableau, Superset, Grafana (with project catalog screenshot)

• .yaml: Docker, CI/CD

Advantages:

• Time Series analysis

• Experience in credit scoring

• Backend development skills in Python

• Participation in Kaggle (link welcomed)

• Basic knowledge of Deep Learning (DL)