About This Opportunity
M-KOPA has redefined financial inclusion across Africa by combining smartphone and asset financing with sophisticated mobile credit, building a customer base of over 3 million people across Kenya, Uganda, Ghana, Nigeria, and South Africa. The company's model — enabling customers to pay for smartphones, solar systems, and other productive assets in small daily micro-payments — generates a rich, proprietary repayment dataset that fuels some of the most innovative credit scoring work happening anywhere on the continent.
This Data Scientist role sits at the core of M-KOPA's commercial strategy. The models you build will directly determine who receives credit, at what price, and under what risk parameters — decisions that simultaneously affect commercial performance and financial access for people at the base of the economic pyramid. This is high-stakes, high-impact data science in the truest sense of the phrase.
M-KOPA is backed by global investors including Sumitomo Corporation, Standard Bank, and the CDC Group, and has built one of the most technically sophisticated data science organisations in African fintech — making it an exceptional career environment for a specialist who wants both technical depth and social impact.
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
Applying for This Role
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
Location & Benefits
Fully remote 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
M-KOPA is a connected asset financing platform that delivers affordable solar energy, smartphones, and digital financial services to underserved consumers across sub-Saharan Africa. By combining IoT technology with flexible pay-as-you-go financing, M-KOPA has transformed the lives of over three million customers who previously lacked access to formal credit or reliable energy.
Data teams want analysts who can formulate the right question before touching a query. Technical fluency (SQL, Python, visualisation tools) is the baseline — the real differentiator is whether you can translate findings into decisions that non-technical stakeholders will act on.
Full-time roles typically include benefits (health insurance, pension contributions, paid leave). During salary negotiation, always consider the total compensation package — benefits can be worth 20–30% on top of base salary. Ask specifically about probation period, performance review cadence, and remote/hybrid flexibility before signing.
Remote roles give you location freedom but require strong self-management. Before accepting, confirm: What are the core overlap hours? Which collaboration tools does the team use (Slack, Notion, Linear, Figma)? Is there a home-office stipend? How does the team handle onboarding for remote hires? Remote-first companies typically have better async culture than companies that went remote reluctantly — ask how decisions get documented.
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