Rural Business Finance Analytics Lead
4 days ago
Business Analytics Manager - Rural Finance Strategy
About the RoleWe are seeking seasoned professionals with expertise in modeling and analytics to drive business growth by integrating insights into end-to-end solutions. By translating data-driven results into actionable deliverables, you will play a key role in enabling strategic decision-making and shaping the future of rural finance lending.
Key Responsibilities- Analyze large datasets to identify trends, patterns, and correlations related to loan performance, customer behavior, and market dynamics.
- Develop and maintain predictive models to assess credit risk, predict default probabilities, and optimize loan approval & collection processes.
- Collaborate with product development teams to design and launch micro loan products (Gold Loan, Business Loan, Rural Loan against Property).
- Conduct ad-hoc analyses to support strategic initiatives such as product development, pricing optimization, and market segmentation.
- Provide actionable insights to improve underwriting criteria, loan origination processes, and customer targeting strategies.
- Stay up to date with industry trends, best practices, and regulatory changes impacting rural business lending.
- Collaborate with IT and data engineering teams to ensure data quality, integrity, and accessibility.
- Lead the development of model methodologies, algorithms, and diagnostic tools for testing model robustness, sensitivity, and stability.
- 6+ years of experience with R, Python, PL/SQL.
- Prior experience in BFSI domain is a strong advantage.
- Good understanding of Microfinance Credit Bureau data.
- Proficiency in statistics and analytics tools R, Python, SAS.
- Familiarity with relational databases and intermediate SQL.
- Ability to synthesize unstructured data and extract insights using SAS/Python/R.
- Experience with Google Cloud Platform, BigQuery, Vertex AI is a huge advantage.
- Strong problem-solving skills with ability to connect business requirements to data-driven solutions.
- Knowledge in one or more areas: Logistic Regression, Linear Regression, Stochastic Models, Bayesian Modeling, Classification, Clustering, Neural Networks, Non-parametric Methods, Multivariate Statistics.
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