Data Scientist – Credit Eligibility at M-KOPA

M-KOPA is a fast-growing FinTech company offering millions of underbanked customers across Africa access to life-enhancing products and services. From our roots as the pioneer in pay-as-you-go PayGo solar energy for off-grid homes, we have grown into one of the most advanced connected asset financing platforms in the world, empowering a broad range of customers to achieve progress in their lives.

We're looking for a 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

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 — 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.

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