Analyze credit applications, customer profiles, and financial data to assess risk.
Interpret credit scoring models, behavioral data, and alternative data sources to support credit decisions.
Assist in setting and refining credit policies and underwriting criteria.
Develop and maintain NPV valuation (financial) models for lending products, credit strategies, and portfolio performance.
Monitor performance metrics such as delinquency, default rates, and recovery.
Conduct cohort and segmentation analysis to identify trends and early warning indicators.
Support the development of risk dashboards and reporting tools.
Design, execute, and analyze lending campaigns aimed at driving credit usage, improving repayment behavior, or reducing risk.
Partner with business, marketing, CRM, and product teams to ensure alignment of campaign goals with credit policies.
Monitor campaign performance and provide recommendations for optimization.
Work with business, product, engineering, and collections teams to design and implement lending workflows.
Provide analytical support during new product rollouts or changes in credit policy.
Participate in cross-functional discussions on customer experience and product risk.
Translate business problems to statistical problems and leverage machine learning technology to solve business problems.
Experiment with new tools, techniques, algorithms and methodologies to enhance the bank’s capabilities
Requirements
Bachelor’s degree or higher in applied mathematics, statistics, computer science, physics, data science, economics, engineering or equivalent quantitative discipline
Relevant work Experience of 2-5 years
Proficient in SQL and Python for data manipulation and analyze large datasets
Proficiency in Excel, and/or data visualization tools (e.g., Tableau, Power BI)
Experience in consumer lending (credit cards or personal loans or BNPL) is required