Role purpose
As a Credit Data Scientist, you’ll use data, feature engineering and experimentation to improve credit decisioning and portfolio performance across our lending products and markets. You’ll work end\-to\-end from data exploration through to production\-aligned features, monitoring and impact measurement.
Key responsibilities* 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).
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.
Nice to have* Experience with bureau data, open banking/transactional data, device/behavioural signals, or alternative data.
- Familiarity with model monitoring, governance, and documentation practices in regulated environments.
- Exposure to cloud analytics stacks (e.g., BigQuery/Snowflake/Databricks) and version control (Git based).
Personal attributes* Curious and pragmatic; focused on measurable outcomes.
- Comfortable working in detail and iterating quickly while maintaining quality.
- Collaborative and able to work across markets and time zones.
Reporting line and location* Reports into credit analytics center of excelence.
- Location: Bengaluru, India. With collaboration with in\-country lending and credit risk teams.