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
  

• 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