Manager/Deputy Manager - Analytics

4 - 8 years

10 - 15 Lacs

Posted:1 day ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Role & responsibilities :

Key highlights of the role are listed below (purely indicative and not limiting):

This position would include the mentioned set of responsibilities but not limited to:

  • Strong statistical knowledge and demonstrated hands-on experience in the domain of credit risk
  • Develop and maintain application, behavioural, and collections scorecards for credit cards.
  • Expertise in ECL modelling: PD/LGD/EAD estimation, staging logic, lifetime loss forecasting, macroeconomic overlays.
  • Perform portfolio analytics such as delinquency trends, vintage analysis, revolve rate, and charge-off forecasting.
  • Design risk-based limit assignment strategies and optimize credit line management.
  • Collaborate with product, risk, and collections teams to implement data-driven approval and decline strategies.
  • Conduct stress testing and scenario analysis for credit card portfolios.
  • Ensure compliance with RBI guidelines, Basel norms, and internal governance standards.
  • Prepare model documentation and presentations for senior management and regulatory reviews.

Applicants should possess the following attributes:

  • Strong experience in credit card risk modelling and unsecured lending.
  • Have previously worked on models like Fraud risk models, attrition/retention models, vintage analysis, stress testing etc
  • Expertise in ECL modelling: PD/LGD/EAD estimation, staging logic, lifetime loss forecasting.
  • Solid understanding of credit bureau data (CIBIL, Experian, CRIF) and fraud risk analytics.
  • Knowledge of regulatory requirements (RBI, Basel norms, IFRS 9).
  • Excellent analytical, problem-solving, and communication skills.
  • Strong proficiency in SAS, Python, R, and SQL for data analysis and model development
  • Experience with statistical techniques (Logistic Regression, Decision Trees, Scorecards)
  • Familiarity with machine learning methods for advanced risk modelling
  • Ability to work with large datasets and credit bureau data (CIBIL, Experian, CRIF).

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