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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand and metro location increase applicants, though credit-risk specialization reduces broad competition.
Deep lending and credit-risk domain expertise required limits cross-industry transferability.
Mandatory credit-risk domain expertise plus SQL/Python/BRE requirements create strict shortlisting filters.
Job Description
Structured overview of role & requirementsAbout This Role
Lead and execute risk analytics for digital unsecured lending portfolios including BNPL and Personal Loans, focusing on portfolio performance and emerging credit risks.
Develop and implement credit risk strategies by partnering with cross-functional teams and evaluating credit policies using data-driven insights.
Use advanced data tools and modeling techniques to monitor portfolio metrics, conduct root cause analyses, and present actionable risk recommendations to senior stakeholders.
Minimum Requirements
Experience in Credit Risk, Risk Analytics, Credit Risk Policy, or Decision Science, preferably in digital lending portfolios like BNPL or Personal Loans.
Proficiency in SQL, data mining tools (Hive, Metabase), and Python for data analysis and risk rule implementation.
Strong understanding of credit risk metrics (DPD, roll rates, GNPA/NPA, vintage curves) and PD, EAD, LGD modeling related to Expected Credit Loss.
MBA, Engineering, or Master's in Statistics, Data Science, or related quantitative field.
Ideal Candidate Profile
Demonstrated ability to independently drive projects and navigate ambiguity in fast-paced digital lending environments.
Strong analytical and critical thinking skills to translate complex risk data into clear, actionable business insights.
Experience collaborating effectively with cross-functional teams (Business, Product, Data Science) to shape credit risk strategies and policies.