Senior Data Scientist - Credit

M-KOPA
NairobiPosted 26 March 2026

Job Description

Senior Data Scientist - Credit [https://app.ashbyhq.com/api/images/user-content/1d985516-2a99-43c0-b591-957f5adaceb9/5ac97b31-49cd-4012-8bb0-5caf15e33c69/B25.png] We're looking for a Senior Data Scientist who loves building predictive models and solving ambiguous data problems. You'll own the models that shape loan eligibility and pricing across 5 African markets. This is a small team with big responsibility, where your work directly shapes lending strategy for millions of customers. [https://app.ashbyhq.com/api/images/user-content/1d985516-2a99-43c0-b591-957f5adaceb9/5375c5bf-f24e-4319-9c68-0cb14bd4455e/image.png] The Impact Your models will directly shape how millions of underserved customers access credit for the first time. We've already helped over 7 million customers access over $2 billion in credit - and we process over 1.5 million payments daily. It's your chance to be part of something that's literally transforming lives across an entire continent ๐ŸŒ [https://app.ashbyhq.com/api/images/user-content/1d985516-2a99-43c0-b591-957f5adaceb9/b41499a5-ef07-4510-ad7a-4346073634b1/image.png] The Opportunity - ๐ŸŽฏ Mission-driven data science: Build credit scoring and pricing models that expand financial access for customers traditionally excluded from formal lending - ๐Ÿ† Global recognition: Join a company named by TIME 100 as one of the world's most influential and by the Financial Times as Africa's fastest-growing for 4 consecutive years (2022โ€“2025) - ๐Ÿš€ Scale challenges: Work with rich repayment datasets across 5 African markets, developing ML models that balance growth with credit risk at scale - ๐ŸŒฑ Environmental impact: We're carbon-negative, having displaced over 2.1 million tonnes of emissions What You'll Do At M-KOPA, you'll build and refine the predictive models that power our lending strategy. You'll sit within a small, high-performing team with end-to-end ownership of credit scoring, loan eligibility, and pricing optimisation โ€” working cross-functionally with engineers, analysts, growth managers, and commercial stakeholders across multiple countries. Join us in combining cutting-edge data science with purpose-driven work that makes digital and financial inclusion possible across Africa. Day to day, you'll be: - Building and refining credit scoring models that assess customer creditworthiness, default risk, and loan pricing across multiple markets - Developing and testing ML models for loan eligibility and pricing optimisation through A/B testing and statistical analysis - Continuously improving eligibility criteria by analysing repayment data, engineering new features, and monitoring credit performance for risk shifts and margin impact - Collaborating cross-functionally with engineers, data scientists, and commercial stakeholders to scale models into production Technical Environment ๐Ÿ’ป - Languages & Libraries: Python, SQL, scikit-learn, pandas, numpy, and relevant ML libraries - Techniques: Predictive modelling, classification/regression, feature engineering, model selection, hyperparameter tuning, A/B testing - Domain: Credit scoring, underwriting, loan pricing, risk analytics Our Team Approach - Low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact - High degree of ownership over your domain โ€” you're empowered to make data-driven decisions and prioritise solutions - Cross-functional collaboration with engineering, product, and commercial teams across multiple countries - Analytical rigour combined with deep market understanding to serve customers excluded from formal financial services What You Need Credit accessibility and affordability are at the core of this role. You'll join a small, high-performing team where every day brings new modelling challenges and analyses that shape our lending strategy. If building models that can transform financial access for millions of African customers excites you, we'd love to hear from you. R ... (truncated, view full listing at source)
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