Website M-KOPA Solar
M-KOPA’s mission is to make high quality energy affordable to everyone. OUR GROWTH SO FAR… 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. Each 8W battery powered-system comes with three lights, m… read moreobile phone-charging and a solar powered radio. Customers can now opt for a 20W system with digital TV. As of July 2016, M-KOPA has connected over 400,000 homes to affordable solar power. Current customers will make projected savings of US$ 300 Million over the next four years. M-KOPA’s customers will enjoy 50 million hours of kerosene-free lighting per month. Total employment created in East Africa is 2,500. In March 2016, M-KOPA emerged boldest at Financial Times Arcelor Mittal- Boldness in Business Awards in the Developing Markets category. In February 2016, M-KOPA was recognised as the Best Mobile Innovation for Emerging Markets at the Global Mobile Awards. In 2015, M-KOPA was recognised by Fortune Magazine as one of the Top 50 Companies Changing The World and won the Zayed Energy Future Prize. M-KOPA has also won the 2014 Bloomberg Pioneer Award and 2013 FT/IFC Excellence in Sustainable Finance Award.
What You’ll Do
At M-KOPA, you’ll own the analytical work that drives our lending strategy. Our Credit Eligibility team operates with a high degree of autonomy — you’ll work cross-functionally with engineers, data scientists, growth managers, and commercial stakeholders across multiple countries, bringing analytical rigour to decisions that shape both credit performance and customer outcomes.
Analyse M-KOPA’s repayments data and other data sources to continuously improve credit scorecards and eligibility criteria while managing credit risk
Refine loan pricing based on credit analysis and customer behaviour
Test new loan types to understand customer demand and credit performance
Monitor credit performance to detect risk shifts and quantify margin impact
Test the predictiveness of new data sets for eligibility criteria purposes
Use Python, SQL, and other tools to drive data insights
Work with data scientists to leverage machine learning models as part of loan eligibility decisions
Your Technical Environment
Languages & tools: Python, SQL, and other analytical tooling
Data: Repayments data, customer behaviour data, third-party data sets
Modelling: Risk modelling and statistical analysis across large, complex data sets
Collaboration: Cross-functional work with engineers, data scientists, analysts, growth managers, and commercial stakeholders
Context: Credit, underwriting, and lending analytics in emerging markets
Our Team Approach
Low-ego, high-impact: We foster a collaborative environment where diversity, innovation, and rigour drive both commercial growth and social impact
Data-driven decision-making: You’ll be empowered to make the case for prioritisation and own the analysis that shapes lending strategy
Ownership: You’ll have a high degree of ownership over your domain – and the responsibility that comes with it
Ambiguous problems welcome: We look for people who thrive when the problem isn’t fully defined yet
Newly established, fast-expanding: You’ll be joining at a formative moment for our credit and underwriting capabilities
What You Need
We’re looking for someone with experience in roles with significant analytical components who is ready to take real ownership of credit and eligibility decisions at scale.
Required experience:
Strong statistical modelling and quantitative analysis skills, including the ability to conduct your own analysis of unstructured data
Experience in credit, underwriting, or lending analytics
Fluency in Python, SQL, and other relevant analytical tools
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
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