I am actively hiring candidates for middle/senior data scientist positions in the model development team for Alfa-Bank's corporate clients.

We are currently looking for individuals to develop the bank's recommendation engine for products in digital channels and to assess potential customer profitability for the bank (CLTV), but there are also other ambitious and interesting tasks.

Position: Middle/Senior Data Scientist Where? Alfa-Bank, Model Development Department, Directorate of Advanced Analytics for Legal Entities. Work Format: Hybrid: Moscow, m. Technopark + remote.

What needs to be done?

1. Analysis of unstructured data in Hadoop, creation of new features and their application in models.
2. Experiments with non-standard approaches for modeling compared to classical scoring.
3. Analysis of the effectiveness of new data sources, testing of credit risk models on new data sources.
4. Full cycle of development and productization of classical ML models: — Participation in regular video calls with business customers. Formation of a basic technical specification together with the customer; — Conducting preliminary analytics, demonstrating results to the customer, adjusting requirements; — Forming customer expectations regarding the final outcome and expected timelines for development, testing, and implementation of the solution; — Development of ML model(s) to solve the business problem from scratch, i.e., forming a general dataset and target event using SQL queries in the database, developing one or more ML models for the most optimal solution to the problem, and if necessary, developing additional features to improve forecast quality.
5. Preparation of materials (presentations) for demonstrating work results.
6. Productization of the model solution.

Bonus: A friendly professional team of over 30 Data Scientists working in various areas related to the development of processes for the corporate sector of the economy.

Requirements: — Physics and Mathematics, Economics and Mathematics education. Good knowledge of probability theory and mathematical statistics; — Ability to work with data (analysis, cleaning, preparation, selection, and feature engineering), SQL query writing skills. Experience working with Hadoop (Hive, Impala, Apache Spark). — Knowledge of Python (specifically, libraries used for data analysis: scipy, pandas, scikit-learn, xgboost, lightgbm, etc.); — Experience in developing classical ML models (linear, trees, boosting, etc.). Knowledge of data processing methods (preparing datasets for training), quality evaluation metrics. Understanding of the principles of classical ML model algorithms. — Communication skills: Required skills (experience) in communicating with business customers, i.e., individuals or groups who do not have ML expertise or deep analytical competencies, but who understand the needs of processes for enhancement with ML models.