Utilize data and machine learning for credit decisioning and portfolio performance across lending products while collaborating with teams on governance and model documentation.
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.
· 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).
Requirements
· 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.
· 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).
· 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.
· Reports into credit analytics center of excelence.
· Location: Mumbai, India. With collaboration with in-country lending and credit risk teams.
TymeX builds the technology and intelligence layer for digital banking, focusing on creating adaptive and personalized experiences for users. Serving multiple countries in Asia and beyond, TymeX develops software and risk management solutions that empower financial institutions to enhance safety and efficiency in banking operations. With a commitment to leveraging advanced data and AI, the company’s innovations play a crucial role in combatting financial crimes and streamlining banking processes.
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