Senior Data Scientist - Credit at M-KOPA

• 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
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• 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