Description

Our team solves a wide range of tasks related to optimizing Pre-collection and recovery strategies, and improving the efficiency of existing processes. Our goal is to use statistics and analytics to select the fastest and most effective way for the client to resolve difficulties encountered in servicing their credit debt.

You will have the opportunity to analyze the largest retail portfolio in the Russian Federation, formulate research hypotheses, launch A/B tests, evaluate effects, identify growth areas, and implement proposals aimed at improving the efficiency of recovery and Pre-collection strategies.

Responsibilities

• formulating hypotheses to improve the efficiency of the recovery process, organizing their testing, preparing conclusions and proposals for changes to risk strategies.
• developing proposals to improve the logic, develop the technological toolkit for applying risk strategies/models, and its interaction with external sources.
• interacting with business units to defend changes, pilot results, system modifications, and projects.
• developing and maintaining portfolio and operational reporting.
• searching for, researching, analyzing, and piloting new tools and best practices in recovery processes (social networks, big data, machine-learning).
• portfolio risk management of the retail credit portfolio in the recovery segment, launching and adjusting procedures for automatic multi-factor analysis of the Early Recovery portfolio.
• ensuring compliance of business plan performance indicators with target values, identifying reasons for deviations.
• the ability to find solutions to complex analytical cases related to portfolio analysis, backtesting model analytics to improve strategies.

Requirements

• higher education (Physics/Mathematics/Technical);
• advanced knowledge of SQL, GreenPlum (ability to write complex queries and optimize SQL code), Python;
• experience in solving complex analytical tasks, skills in working with large data sets;

Will be a plus:

• experience in a bank's risk department / recovery units / collection agency;
• experience in developing and automating customer interaction strategies, developing business requirements, testing and accepting new system functionality;
• experience in supporting machine learning models throughout their entire lifecycle, including interaction with technical teams (DE, DS, Dev).

Conditions

• comfortable modern office near Kutuzovskaya metro station

• office-based work format

• annual salary review, quarterly and annual bonus (the bonus is specified depending on the position and bonus system)

• corporate gym and relaxation areas

• more than 400 educational programs from SberUniversity for professional and career development

• adaptation program and manager assistance at the start (for Junior positions)

• extended voluntary health insurance, preferential insurance for family, and corporate pension program

• mortgage more favorable by up to 7% for each employee

• free SberPrime+ subscription, discounts on products from partner companies

• reward for recommending friends to the Sber team.