Senior Data Scientist - Credit Modeling 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.

• You’ll be joining a team that’s rapidly expanding our credit and underwriting capabilities. We are looking for someone who loves building predictive models, analyzing complex data, and solving challenging, ambiguous data problems — if that sounds like you, you might be a fit!

In this role, you would be responsible for:

• Building and refining credit scoring models to assess customer creditworthiness and default risk
• Analyzing M-KOPA’s repayments data and other data sources to continuously improve our loan eligibility criteria while managing credit risk
• Developing machine learning models for loan eligibility decisions and pricing optimization
• Refining loan pricing based on credit analysis, predictive modeling, and customer behavior
• Testing new types of loans to understand customer demand and credit performance through A/B testing and statistical analysis
• Monitoring credit performance to detect risk shifts and quantify margin impact using advanced analytics
• Testing the predictiveness of new data sets and feature engineering for enhanced model performance
• Using Python, SQL, and other tools for data analysis and model development
• Collaborating with data scientists to implement and scale machine learning models in production

This role can be remote or hybrid, but candidates must be located within our time zones (UTC -1 to UTC+3) to ensure effective collaboration with teams across our multiple locations.

Your application should demonstrate:

• Several years of experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems
• Strong machine learning background with experience in model development, validation, and deployment
• Advanced statistical modeling and quantitative analysis skills, including experience with model evaluation metrics and performance monitoring
• Proficiency in Python, SQL, and relevant ML libraries (scikit-learn, pandas, numpy, etc.)
• Experience with 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
• Strong interpersonal and collaboration skills
• (Highly desirable) Experience in credit, underwriting, lending analytics, or fintech modeling

Method of Application