Information TechnologyFull-TimeSenior-level(6+ yrs)
Job Description
M-KOPA’s mission is to make high-quality energy affordable to everyone. 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.
Role Overview
In this role, you will be responsible for building and refining credit scoring models to assess customer creditworthiness and default risk. You will analyze M-KOPA’s repayments data and other data sources to continuously improve loan eligibility criteria while managing credit risk. You will also develop machine learning models for loan eligibility decisions and pricing optimization.
Responsibilities
Build and refine credit scoring models to assess customer creditworthiness and default risk.
Analyze M-KOPA’s repayments data and other data sources to continuously improve loan eligibility criteria while managing credit risk.
Develop machine learning models for loan eligibility decisions and pricing optimization.
Refine loan pricing based on credit analysis, predictive modeling, and customer behavior.
Test new types of loans to understand customer demand and credit performance through A/B testing and statistical analysis.
Monitor credit performance to detect risk shifts and quantify margin impact using advanced analytics.
Test the predictiveness of new data sets and perform feature engineering for enhanced model performance.
Use Python, SQL, and other tools for data analysis and model development.
Collaborate with data scientists to implement and scale machine learning models in production.
Requirements & Qualifications
BA/BSc/HND degree in a quantitative field (e.g., Statistics, Computer Science, Economics).
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) and interpersonal skills.
(Highly desirable) Experience in credit, underwriting, lending analytics, or fintech modeling.
Working Location
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.
