Data Scientist - Lending

Overview

About INDmoney:

INDmoney is a fast-growing super finance app revolutionizing the way users manage their investments and finances. As we scale our digital lending vertical, we seek dynamic professionals with a strong background in ML & data science to create, enhance and monitor our risk models and ensure robust underwriting decisions to scale business.

Job Summary:

We are looking for a Data Scientist, with a hybrid background in Data science, Credit Risk model development, Machine Learning and Data analysis. The ideal candidate will build intelligent, data-driven credit risk models with predictive modeling expertise using machine learning & AI. He will play a key role in refining credit strategies, reducing defaults, and driving healthy loan book growth.

Key Responsibilities:

• Develop, validate, and implement credit risk models using credit bureau data, demographic indicators, transactional behavior, and alternative data sources.
• Leverage machine learning, Mathematical and statistical techniques to predict borrower behavior, default probability, and fraud risk.
• Design and refine scorecards and decision engines to automate and scale credit decisions.
• Monitor loan portfolio performance, identify risk trends, and recommend optimization strategies.
• Detect and mitigate fraud by spotting suspicious patterns using advanced analytics.
• Collaborate with Product, and Tech teams to ensure seamless integration of risk models into the lending journey.
• Stay updated on RBI digital lending guidelines and ensure regulatory compliance across risk models.

Qualifications & Skills:

• Bachelor’s/Master’s degree in Finance, Economics, Data Science, Statistics, or a related field.
• 3–5 years in Data Science and Credit Risk Analysis.

Must have: The candidate must have an experience in using data science for building lending credit risk models

• Hands-on experience building predictive models for credit risk using tools like Python, R, or similar.
• Machine Learning · Data Science · · Data Analytics · Python (Programming Language) · Data Visualization · SQL · Statistical Modeling
• Knowledge of credit bureau reports, financial statement analysis, and alternative credit scoring techniques.
• Experience with fraud detection models and risk-based pricing strategies is a plus.
• Strong problem-solving skills and a keen eye for data patterns and business impact.