AM/M - Risk Analytics - User Lending

Owning the risk for one or more segments of the portfolio while maintaining a healthy growth. Analyzing credit policies using Meesho platform data, bureau data, SMS data, and other alternate data. Creating policies, scorecards and models (Application Score, B-Score, Collection Scores) for BNPL and PL. Designing experiments to test cohort behaviour, experimental policies, and new opportunities for growth. Identifying pockets of risk within the portfolio that don't make sense, and identifying leading indicators / EWS. Automating reporting and tracking of the portfolio at multiple levels - Ops performance measures, key performance indicators, and their drivers. Refining credit policies based on data and creating new policies for unexplored segments, including building day-1 policies for Personal Loans as the product scales. Configuring and testing rules in the real-time Business Rule Engine (BRE), and working with the Bureau Feature Store to define new underwriting features. • Own and drive risk analytics initiatives across digital lending portfolios, including BNPL, Personal Loans, and other unsecured lending products. • Identify portfolio trends, diagnose emerging risks, and conduct deep-dive RCAs to understand key drivers of credit performance. • Partner closely with Business, Product, Data Science, and other cross-functional teams to conceptualize and execute risk strategies and projects. • Evaluate existing and proposed credit policies and translate portfolio insights into actionable risk interventions. • Monitor key portfolio metrics including 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 relevant 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 and compellingly to senior stakeholders, both verbally and in written form. • Work in a fast-paced environment, independently drive projects, and solve ambiguous business and risk problems using first-principles thinking. • Leverage AI tools in day-to-day analytics, problem-so

About the Team Would you like to be part of creating a unicorn within a unicorn? Solve some of the biggest challenges facing financial inclusion of Bharat? Well, then here's your chance! Financial Services (FS) is one of the biggest new bets we are taking at Meesho on the next 10x horizon opportunity for us. Just as Ant Financial did for Alibaba, and Mercado Pago did for Mercado Libre, our vision is to build financial services for Meesho, becoming our key growth and revenue driver. We have found our Product-Market Fit, built and proved 0-1 and 1-10 journeys. Now looking for someone who can be the founding member of a 10-100-1000 journey, and this is possibly one of the most exciting and enriching experiences one could consider in their career. About the Role Meesho FS runs two lines of business: SME Lending, which offers Business Loans, and User Lending, which offers BNPL and Personal Loans (PL).  User Lending is now our primary focus in this phase of our FS journey. We have 274Mn ATU, and risk management is at the core of building any sustainable credit business at scale - our being a captive audience means both Growth and Risk are an outcome of underwriting, which is owned by the Risk team. Herein also lies one of our biggest challenges: how do we assess the risk of borrowers without any credit history or income data? How can we leverage alternative information to better profile borrowers? How can we leverage our platform uniquely to create a sustained competitive advantage? This person will be responsible for driving the analytics behind these questions for User Lending - creating analytical frameworks, developing high quality analysis, driving insights and actions, and recommending policy decisions, across a well-established BNPL portfolio and a Personal Loans portfolio that is still scaling. Your role will focus on: Owning the risk for one or more segments of the portfolio while maintaining a healthy growth. Analyzing credit policies using Meesho platform data, bureau data, SMS data, and other alternate data. Creating policies, scorecards and models (Application Score, B-Score, Collection Scores) for BNPL and PL. Designing experiments to test cohort behaviour, experimental policies, and new opportunities for growth. Identifying pockets of risk within the portfolio that don't make sense, and identifying leading indicators / EWS. Automating reporting and tracking of the portfolio at multiple levels - Ops performance measures, key performance indicators, and their drivers. Refining credit policies based on data and creating new policies for unexplored segments, including building day-1 policies for Personal Loans as the product scales. Configuring and testing rules in the real-time Business Rule Engine (BRE), and working with the Bureau Feature Store to define new underwriting features. What you will do • Own and drive risk analytics initiatives across digital lending portfolios, including BNPL, Personal Loans, and other unsecured lending products. • Identify portfolio trends, diagnose emerging risks, and conduct deep-dive RCAs to understand key drivers of credit performance. • Partner closely with Business, Product, Data Science, and other cross-functional teams to conceptualize and execute risk strategies and projects. • Evaluate existing and proposed credit policies and translate portfolio insights into actionable risk interventions. • Monitor key portfolio metrics including 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 relevant 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 and compellingly to senior stakeholders, both verbally and in written form. • Work in a fast-paced environment, independently drive projects, and solve ambiguous business and risk problems using first-principles thinking. • Leverage AI tools in day-to-day analytics, problem-solving, and productivity workflows. What you will need • 3.5 years to 8 years of relevant experience is required. • 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, relevant experience in Credit Risk, Risk Analytics, Credit Risk Policy, or Decision Science, preferably within digital lending. About us  Welcom