Background

M-KOPA is a leading fintech and technology company that has transformed access to digital financial services across Africa. The organization provides affordable smartphones and other digital financial products to underserved customers using an innovative pay-as-you-go financing model. Through the use of advanced data analytics, artificial intelligence, and mobile technology, M-KOPA enables millions of people to access essential services, build credit histories, and improve their financial wellbeing.

Operating across several African markets including Kenya, Uganda, Nigeria, Ghana, Rwanda, and South Africa, M-KOPA continues to expand its technology-driven financial ecosystem. The company has built a strong reputation for combining financial inclusion with cutting-edge technology solutions that empower customers to participate more fully in the digital economy.

To support its continued growth and expansion across the continent, M-KOPA is currently recruiting for several strategic and technical positions across multiple departments. These roles span areas such as technology operations, data science, product development, marketing, customer operations, enterprise systems architecture, and people management.

Role Context

The Data Scientist specializing in Credit Modeling focuses on developing advanced analytics models that support credit risk assessment and financial decision-making. The role requires expertise in machine learning, statistical modeling, and financial analytics.

Responsibilities

• Develop predictive credit scoring models that support customer lending decisions.
• Analyze large datasets to identify patterns that influence credit risk.
• Design machine learning models that improve credit eligibility assessments.
• Collaborate with risk management teams to refine credit evaluation processes.
• Develop algorithms that support automated lending decisions.
• Monitor model performance and continuously improve predictive accuracy.
• Implement data pipelines that support analytics and machine learning operations.
• Conduct research on emerging modeling techniques that improve credit risk analysis.
• Support regulatory reporting and compliance related to credit risk management.
• Provide insights that support strategic decision-making in financial services.

• Develop predictive credit scoring models that support customer lending decisions.
• Analyze large datasets to identify patterns that influence credit risk.
• Design machine learning models that improve credit eligibility assessments.
• Collaborate with risk management teams to refine credit evaluation processes.
• Develop algorithms that support automated lending decisions.
• Monitor model performance and continuously improve predictive accuracy.
• Implement data pipelines that support analytics and machine learning operations.
• Conduct research on emerging modeling techniques that improve credit risk analysis.
• Support regulatory reporting and compliance related to credit risk management.
• Provide insights that support strategic decision-making in financial services.

• Machine learning
• Statistical modeling
• Financial analytics
• Data analysis
• Algorithm development
• Data pipelines
• Credit risk assessment

• Expertise in machine learning
• Expertise in statistical modeling
• Expertise in financial analytics