Background information about the job or company (e.g., role context, company overview)

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.

At M-KOPA, you'll build and refine the machine learning and credit risk 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.

Responsibilities or duties

Day to day, you'll be:

• Building and refining credit scoring and risk modelling solutions 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

Qualifications or requirements (e.g., education, skills)

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.

Experience needed

• 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

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

• Highly Desirable: Experience in credit, underwriting, lending analytics, or fintech modelling
• Location & Benefits: Fully remote Data Scientist role within UTC -1 to UTC +3 time zones
• Work with diverse teams across UK, Europe, and Africa
• Professional development programmes and coaching partnerships
• Family-friendly policies and flexible working arrangements
• Well-being support and career growth opportunities

• Building and refining credit scoring and risk modelling solutions 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 (Highly Desirable)