1. RESPONSIBILITIES
Monitor the Portfolio: Build and maintain the reports and dashboards that track the health of the lending book - approval rates, funnel conversion, and delinquency by cohort.
Investigate: When a number moves and nobody knows why, you're the detective - deep-dive the data, separate real risk changes from mix shifts and data artifacts, and land on the root cause.
Own the Data Foundation: Keep core data assets - the end-to-end lending funnel and portfolio labels - correct, reconcilable, and trusted; chase reliability issues in partner data and drive the fixes.
Shape Underwriting Strategy: Turn data analysis into actionable recommendations for whitelist expansion and the refinement of underwriting rules.
Collaborate on Models & Policy: Analyze credit model performance with
data scientists and review new lending policies to point out flaws and opportunities before going live.
Communicate Insights: Translate findings into clear, actionable insights for business, product, engineering, and data science teams daily.
2. CANDIDATE REQUIREMENTS
Technical Skills (Must-have): Strong SQL and working Python proficiency (depth scales with level).
Analytical & Problem-Solving Mindset: Ability to break down ambiguous problems, form hypotheses, and use data to find root causes - validating data before trusting it.
English Proficiency: Proficient English skills, equivalent to an IELTS score of 6.5 or higher.
Nice-to-have:
Consumer lending / credit risk analytics: funnel and approval analytics, DPD/MOB cohorts, vintage and roll-rate analysis.
Credit model validation and backtesting.
E-wallet, fintech, or payments background.
Dashboarding (Tableau, Power BI, Looker) and pipeline tooling (Airflow, ETL).
APPLY HERE