Data Scientist - Credit Eligibility

Job Description

Requirements

• 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

Responsibilities

• 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
• 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

Benefits

• 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
• Our Impact 💚:
• Connected 📱: 2.5 million first-time smartphone users connected
• Prosperous 💰: 70% of customers use M-KOPA products for income generation, with 35,000 livelihoods created for agents
• Green 🌱: 2.1 million tonnes of CO₂ avoided through clean energy products, with over 127,700 circular economy products provided