Senior Data Scientist - Credit

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. 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 The Opportunity Mission-driven data science : Build credit scoring and pricing models that expand financial access for customers traditionally excluded from formal lending 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 (2022–2025) Scale challenges : Work with rich repayment datasets across 5 African markets, developing ML models that balance growth with credit risk at scale 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 — 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 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