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

Responsibilities

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
• Strong 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

• 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

• Python
• SQL
• scikit-learn
• pandas
• numpy
• Predictive modelling
• Classification/regression
• Feature engineering
• Model selection
• Hyperparameter tuning
• A/B testing
• Credit scoring
• Underwriting
• Loan pricing
• Risk analytics
• Data communication

• Experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems
• Strong 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
• Experience in credit, underwriting, lending analytics, or fintech modelling