- Own dashboards and reporting on portfolio health, disbursements, collections, and unit economics.

- Write and optimize SQL against our loan, application, and payments data.

- Analyse borrower behavior, delinquency, and repayment trends to inform credit policy.

- Run funnel and acquisition analysis with the growth team using Google Analytics.

- Support underwriting with cohort analysis and simple predictive models (e.g. default/propensity scoring).

Required Experience :

- 2 - 3 years in a data analyst role, ideally in fintech, lending, or financial services.

- Strong SQL and hands-on Tableau (or similar BI tool).

- Working knowledge of Google Analytics.

- Exposure to machine learning - you've built or worked with predictive models (Python or R).

- Clear communicator who thrives in an ambiguous, fast-moving environment.

Good to have Skills :

- Consumer or SME credit risk / underwriting / collections analytics.

- Python (pandas, scikit-learn) and cloud data warehouses (BigQuery, Snowflake, Redshift).