About this role
Kuda Bank in Lagos is looking for a Senior Data Analyst focused on credit. The role supports analytics across the credit product life cycle, from design to acquisition, portfolio management, collection and profit and loss. The successful candidate will join the Decision Science team and report to the Credit Risk Analytics Lead, analysing portfolio performance, refining credit policies, building P&L forecasts and conducting risk-based assessments. The position involves communicating insights to senior stakeholders and designing A/B tests to optimise lending and collections. The job is for someone with a strong background in quantitative analysis within financial services or fintech and with solid SQL, Python or R skills.
What you would do
- Analyse portfolio performance metrics to identify trends, risks and opportunities.
- Review and refine credit policies, including rule-based decision engines.
- Build and maintain profit and loss forecasts to guide strategic decisions.
- Conduct credit limit assignments and affordability assessments.
- Perform score cut-off analysis to improve approval and risk strategies.
- Monitor and enhance collection strategies based on segmentation and outcomes.
- Design and run A/B tests to assess and optimise lending and collections performance.
- Communicate actionable insights and recommendations to senior stakeholders.
What they are asking for
- Academic background in a mathematical discipline (e.g. Mathematics, Statistics, Physics, or related field).
- Minimum of 5 years' experience as a Quantitative Analyst or Data Analyst in financial services, fintech or e-commerce.
- Strong proficiency in SQL for data extraction and manipulation.
- Hands-on experience with Python or R.
- Solid understanding of statistical tests used in A/B testing.
- Experience using statistical software packages (open-source preferred).
- Proven experience developing and maintaining analytical reports or dashboards from end-to-end.
- Ability to interpret and present insights from reports in a business context, including stakeholder engagement and recommendations.
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