Kuda is a full-service, app-based digital bank. Our mission is to be the go-to bank not just for those living on the African continent, but also for the African diaspora wherever they might live, anywhere in the world. Kuda is free of ridiculous banking charges and great at helping customers budget, spend smartly, and save more.
As a Data Analyst - Credit, you are supporting the analytics throughout the credit product life cycle, from product design, to credit acquisition, portfolio management, collection and profit & loss. You will work within the Decision Science team, reporting to the Credit Risk Analytics Lead.
Key Responsibilities
- Analyze 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 assignment and affordability assessments
- Perform score cut-off analysis to improve approval and risk strategies
- Monitor and enhance collection strategies based on customer segmentation and outcomes
- Design and run A/B tests to assess and optimize lending and collections performance
- Communicate actionable insights and recommendations to senior stakeholders
Requirements
- Academic background in a mathematical discipline (e.g. Mathematics, Statistics, Physics, or related field)
- Minimum of 3 years' experience as a Quantitative Analyst or Data Analyst in a financial services, fintech or e-commerce environment
- Strong proficiency in SQL for data extraction and manipulation
- Hands-on experience with open-source statistical programming languages such as Python or R
- Solid understanding of statistical tests commonly 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 recommendation
Method of Application
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