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Meesho is seeking a Manager of Risk Analytics to lead credit risk initiatives across its digital lending portfolio, including BNPL, Personal Loans, and other unsecured lending products. This is a hands-on management role focused on portfolio analytics, risk strategy, and cross-functional collaboration.
Key Responsibilities:
- Own and drive risk analytics initiatives across digital lending portfolios (BNPL, Personal Loans, unsecured lending).
- Identify portfolio trends, diagnose emerging risks, and conduct deep-dive root cause analyses (RCAs) to understand credit performance drivers.
- Partner with Business, Product, Data Science, and cross-functional teams to conceptualize and execute risk strategies and projects.
- Evaluate existing and proposed credit policies; translate portfolio insights into actionable risk interventions.
- Monitor key portfolio metrics: DPD buckets, roll rates, vintage curves, GNPA/NPA, collection efficiency, approval rates, losses, and portfolio yield.
- Use data to assess the impact of risk policies and strategies on portfolio growth, credit quality, and profitability.
- Perform data mining and analysis using SQL, Hive, Metabase, Python, and other data tools.
- Build and implement basic scorecards, analytical frameworks, and risk rules in Business Rules Engines (BRE).
- Apply knowledge of PD, EAD, and LGD models to understand and evaluate Expected Credit Loss (ECL) and portfolio-level credit risk.
- Present insights, recommendations, and risk perspectives clearly to senior stakeholders (verbal and written).
- Work independently in a fast-paced environment, solving ambiguous business and risk problems using first-principles thinking.
- 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 (DPD buckets, roll rates, vintage curves, GNPA/NPA, collection efficiency, approval rates, losses, portfolio yield).
- Strong RCA, analytical, and critical-thinking skills; 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 (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 cross-functional teams.
- Strong written and verbal communication skills; 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.
