Credit Data Scientist (Credit Analytics)

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).