M-KOPA is the pioneer and global leader of Connected Asset Financing that offers millions of underbanked customers access to life-enhancing products. Our advanced connected asset financing platform combines digital micropayments and IoT connectivity to offer access to products including solar lighting, televisions, fridges, smartphones, financial services an...
Analyzing M-KOPA's repayments data and other data sources to continuously improve our loan eligibility criteria while managing credit risk
Refining loan pricing based on credit analysis and customer behavior
Testing new types of loans to understand customer demand and credit performance
Monitoring credit performance to detect risk shifts and quantify margin impact
Testing the predictiveness of new data sets for the purposes of eligibility criteria
Using Python, SQL, and other tools for data analysis to drive insights
Working with data scientists to leverage machine learning models as part of loan eligibility decisions
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 in roles with significant analytical components
Strong statistical modeling and quantitative analysis skills, including the ability to conduct your own analysis of unstructured data
Fluency in Python, SQL, and other relevant tools for data analysis
Experience translating complex data insights into actionable business strategies
Ability to work cross-functionally with product, engineering, and commercial teams
Strong data communication skills — written, oral, and visual
Strong interpersonal and collaboration skills
(Nice to have) Experience in credit, underwriting, or lending analytics
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