Data Scientist (Credit Risk)

Shary

In this role you will design and refine credit-risk scorecards that power TRU APP s instant lending decisions, build automated monitoring to spot drift, delinquency and fraud early, and convert data-driven insights into policy updates that keep our rapid growth both profitable and fully compliant.
Key Responsibilities:
• Design, develop & calibrate credit-risk scorecards (application, behavioral & collections) using logistic regression, WOE/IV, and ML uplift.
• Lead champion-challenger testing, back-testing, and full regulatory documentation to meet FRA standards.
• Build automated monitoring dashboards for KS, PSI, and early delinquency alerts, responding quickly to drift.
• Partner with Data Engineering to enrich our feature store and maintain robust data lineage.
• Collaborate with the fraud team to design and refine early-warning models and rules that flag suspicious applications and transactions.
• Analyse portfolio performance (PD, LGD, EAD) and translate insights into credit-policy or limit updates.
• Present findings to leadership and regulators, turning complex analytics into clear, actionable recommendations.
Qualifications:
• 2+ years in data science, credit-risk analytics, or scorecard development (consumer lending, BNPL, cards).
• Proficiency in Python or R, strong SQL, and familiarity with SAS/STAT; hands-on experience with Git & MLOps pipelines.
• Solid grasp of feature engineering, model validation, IFRS 9/Basel frameworks, and FRA guidelines.
• Ability to visualize data with Tableau, Power BI, or matplotlib and craft compelling data stories.
• Degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
• Bonus points for Spark/BigQuery, AWS/GCP, and exposure to fraud analytics or alternative data sources.