Team Manager – Loan Analytics

3 days ago

Mumbai, Maharashtra, India Zyoin Group Full time

Position: Team Manager – Loan Analytics

Function: Rural Business Finance Loan Analytics

Location: Mumbai

We are seeking accomplished professionals with knowledge in modeling, analytics, and financial services . The role involves integrating analytics into end-to-end business solutions and translating data-driven insights into actionable business outcomes.

Key Responsibilities:

  • Analyze large datasets to identify trends, correlations, and patterns 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 teams to design and launch new loan products (Gold Loan, Business Loan, Rural Loan against properties, etc.).
  • Conduct ad-hoc analyses for product development, pricing optimization, and market segmentation.
  • Provide insights to improve underwriting criteria, loan origination processes, and customer targeting strategies.
  • Stay updated with industry trends, regulatory changes, and best practices in rural business lending.
  • Work with IT & data engineering teams to ensure data quality, integrity, and accessibility.
  • Lead the development of model methodologies, algorithms, and diagnostic tools for testing robustness, sensitivity, and stability.

Desired Skills & Experience:

  • 6–10 years of experience in R, Python, PL/SQL (preferably MS SQL Server).
  • Strong background in the BFSI domain (Banks, NBFCs, Microfinance).
  • Good understanding of Micro Finance Credit Bureau data .
  • Proficiency in statistics and tools like R, Python, SAS .
  • Familiarity with SQL and relational databases.
  • Ability to work with unstructured data and derive actionable insights.
  • Experience with Google Cloud Platform, BigQuery, Vertex AI is a plus.
  • Strong problem-solving, business acumen, and ability to link data mining to business impact.
  • Knowledge of at least one advanced modeling technique: Logistic Regression, Linear Regression, Bayesian Modeling, Classification, Clustering, Neural Networks, or Multivariate Statistics.

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