• Apply advanced analytics, machine learning, and statistical modeling to assess and improve credit risk performance across multiple portfolios.
• Develop, validate, and monitor credit scoring models, repayment behavior models, and fraud detection algorithms using Python, R, or similar tools.
• Design and maintain real-time risk dashboards and automated reporting pipelines to support portfolio monitoring and decision-making.
• Lead exploratory data analysis (EDA) to identify trends in borrower behavior, delinquency patterns, and collection effectiveness.
• Collaborate with data engineering and product teams to integrate new data sources (e.g., M-Pesa, alternative credit data, behavioral signals) into core decisioning systems.
• Conduct segmentation and cohort analysis to uncover opportunities for risk-based pricing, credit limit management, and targeted collections strategies.
• Support scenario analysis and risk stress testing to evaluate portfolio resilience under different economic or operational conditions.
• Produce and present actionable insights to the Credit Committee and senior stakeholders on a weekly basis.
• Evaluate the performance of credit policies and collection workflows, and recommend improvements based on data-driven analysis.
• Ensure data quality and integrity through regular audits and contribute to strengthening data governance and model risk management practices.

Deliverables

• Development and deployment of predictive models for credit risk, repayment prediction, and fraud detection.
• Automated credit risk dashboards and reporting tools delivered in collaboration with the data team.
• Integration of at least one new high-impact data source to enrich credit decisioning.
• A detailed risk gap analysis and process optimization report covering credit and collections operations.
• Clear, measurable impact on key credit performance indicators (e.g., reduction in PAR 30+, improved repayment efficiency, increased predictive power of scoring models).
• Internal training materials and sessions delivered on model usage, risk dashboards, and analytics best practices.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Quantitative Finance, Actuarial Science, or a related quantitative discipline.
• Minimum 5 years of experience applying data science and analytics in a financial services or fintech environment, with a focus on credit risk.
• Proficient in Python, R, SQL, and common ML/data science libraries (e.g., scikit-learn, XGBoost, pandas, statsmodels).
• Strong experience in predictive modeling, supervised learning, time series analysis, and A/B testing.
• Familiarity with financial services data (e.g., mobile money, transaction data, credit bureau records) and lending processes.
• Experience working with BI tools for dashboard development and data visualization.
• Excellent written and verbal communication skills, with the ability to translate complex data insights into clear business recommendations.
• Comfortable working in fast-paced, cross-functional teams, and managing multiple analytical projects simultaneousl

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