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Meesho is seeking an Assistant Manager for Risk Analytics to own and drive risk analytics initiatives across digital lending portfolios, including BNPL, Personal Loans, and other unsecured lending products. You will identify portfolio trends, diagnose emerging risks, and conduct deep-dive root cause analyses to understand key drivers of credit performance. Working closely with Business, Product, Data Science, and cross-functional teams, you will conceptualize and execute risk strategies and projects.
Key responsibilities include evaluating existing and proposed credit policies, translating portfolio insights into actionable risk interventions, and monitoring key portfolio metrics such as DPD buckets, roll rates, vintage curves, GNPA/NPA, collection efficiency, approval rates, losses, and portfolio yield. You will use data to assess the impact of risk policies and strategies on portfolio growth, credit quality, and profitability. You will perform data mining and analysis using SQL, Hive, Metabase, Python, and other relevant data tools, and build and implement basic scorecards, analytical frameworks, and risk rules in Business Rules Engines (BRE). You will apply knowledge of PD, EAD, and LGD models to understand and evaluate Expected Credit Loss (ECL) and portfolio-level credit risk.
You will present insights, recommendations, and risk perspectives clearly and compellingly to senior stakeholders, both verbally and in written form. Working in a fast-paced environment, you will independently drive projects and solve ambiguous business and risk problems using first-principles thinking. You will leverage AI tools in day-to-day analytics, problem-solving, and productivity workflows.
REQUIREMENTS:
- Relevant experience in Credit Risk, Risk Analytics, Credit Risk Policy, or Decision Science, preferably within digital lending
- Hands-on experience managing or analyzing portfolios in BNPL, Personal Loans, or other unsecured digital lending businesses
- Strong understanding of credit risk metrics and portfolio performance, including DPD buckets, roll rates, vintage curves, GNPA/NPA, collection efficiency, approval rates, losses, and portfolio yield
- Strong RCA, analytical, and critical-thinking skills, with the ability to translate complex data into clear business insights
- Strong understanding of PD, EAD, and LGD modelling and their application in Expected Credit Loss (ECL)
- Strong working knowledge of SQL and experience with data-mining tools/systems such as Hive, Metabase, or equivalent databases
- Hands-on experience with Python, including data wrangling, basic scorecard development, and implementing/coding risk rules in BRE
- Ability to work effectively with Business, Product, Data Science, and other cross-functional teams
- Strong written and verbal communication skills, with the ability to articulate and influence risk decisions using data and structured thinking
- Experience working in a fast-paced, high-ownership environment with ambiguity
- MBA, Engineering, or Master's degree in Statistics, Data Science, or a related quantitative field
- Demonstrated ability to use AI tools effectively in day-to-day work and problem-solving
