Credit Data Scientist (Credit Analytics) - Bengaluru

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Skills you'll use

What you'll do

• · Analyse customer, bureau, transactional and repayment data to identify drivers of risk, loss, approval rates and customer outcomes.
• · Build and iterate credit risk features and model inputs (behavioural signals, affordability proxies, stability-tested transformations), partnering closely with senior modellers and engineering.
• · Contribute to development and improvement of predictive models using modern machine learning approaches, with a focus on robustness, stability and deployability.
• · Design, run and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews.
• · Develop monitoring for model/policy performance and feature health (drift, stability, segment performance, data quality checks).
• · Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives.
• · Work with Data/Engineering to improve data definitions, quality, lineage and reproducible pipelines; document feature logic and assumptions.
• · Contribute to governance documentation (model inputs, feature catalogues, monitoring evidence, change logs).

Who can apply

• Required experience and qualifications
• · 2–4 years in credit analytics / credit risk / lending data science (bank, fintech, lender, bureau, consulting).
• · Strong Python and/or SQL skills and experience working with large datasets.
• · Proficiency in Python or R for analysis and modelling.
• · Solid grounding in statistics and predictive model evaluation (ranking performance, calibration, stability) and business impact measurement.
• · Exposure to advanced machine learning concepts (e.g., ensemble methods, cross-validation, hyperparameter tuning) and an understanding of how to apply them responsibly in production settings.
• · Clear communication skills with technical and non-technical stakeholders.

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