M-KOPA’s mission is to make high quality energy affordable to everyone. OUR GROWTH SO FAR... M-KOPA has connected more than 400,000 homes in Kenya,Tanzania and Uganda to solar power with over 550 new homes being added every day. 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 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. Required Experience: Experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems ML background with hands-on experience in model development, validation, deployment, and performance monitoring Proficiency in Python, SQL, and relevant ML libraries (scikit-learn, pandas, numpy, etc.) with experience in feature engineering, model selection, and hyperparameter tuning Experience translating complex model outputs into actionable business strategies and stakeholder communications Ability to work cross-functionally with product, engineering, and commercial teams Strong data communication skills — written, oral, and visual Highly Desirable: Experience in credit, underwriting, lending analytics, or fintech modelling