Working on a team, you will determine M-KOPA’s loan eligibility criteria and develop our loan offers and pricing, to ensure that we are offering loans that customers want and can afford while also achieving our business growth objectives.
This role offers the opportunity to drive significant customer and business impact. You will be a member of a small team with the big responsibility of continuously improving M-KOPA’s loan offers and eligibility criteria, in order to drive growth while managing credit losses and margins. You'll work cross-functionally with engineers, data scientists, other analysts, growth managers, and commercial stakeholders across multiple countries.
We foster a low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact. Our team values psychological safety and takes a discovery-first approach to problem solving. You'll be empowered to make data-driven decisions and clear cases for prioritisation of solutions in a domain that you have a high degree of ownership over.
We are looking for someone who loves analysing complex data and solving challenging, ambiguous data problems — if that sounds fun to you, you might be a fit!
For Remote roles:
This is a fully remote role, you would be working within the following time zone (UTC -1 / UTC+3) with a diverse group of other employees working remotely from locations such as UK, Europe and Africa.
In this role, you would be responsible for:
Analysing M-KOPA’s repayments data and other data to continuously improve our loan eligibility criteria
Developing new loan offers and pricing based on customer demand and credit analysis
Monitoring the credit performance of your loans to identify where we need to adjust eligibility criteria and/or pricing
Testing the predictiveness of new data sets for the purposes of eligibility criteria
Testing new types of loans to understand customer demand and credit performance
Working with engineers to implement changes to our eligibility criteria
Working with data scientists to leverage machine learning models as part of loan eligibility decisions
Your application should demonstrate:
Several years in roles with significant analysis components
Strong analytical skills, including the ability to conduct your own analysis of unstructured data
Ability to write your own SQL queries and source your own data sets
Proven ability in solving ambiguous / high-level analytical problems
Strong data communication skills — written, oral, and visual
Ability to work with diverse, distributed teams across multiple countries
Strong interpersonal and collaboration skills
(Nice to have) Experience with data science models
At M-KOPA, we empower our people to own their careers through diverse development programs, coaching partnerships, and on-the-job training. We support individual journeys with family-friendly policies, prioritize well-being, and embrace flexibility.
Join us in shaping the future of M-KOPA as we grow together. Explore more at m-kopa.com.
Recognized thrice by the Financial Times as one Africa's fastest growing companies (2022, 2023 and 2024) and by TIME100 Most influential companies in the world 2023 and 2024 , we've served over 5 million customers, unlocking $1.5 billion in cumulative credit for the unbanked across Africa.
Important Notice
M-KOPA is an equal opportunity and affirmative action employer committed to assembling a diverse, broadly trained staff. Women, minorities, and people with disabilities are strongly encouraged to apply.
M-KOPA explicitly prohibits the use of Forced or Child Labour and respects the rights of its employees to agree to terms and conditions of employment voluntarily, without coercion, and freely terminate their employment on appropriate notice. M-KOPA shall ensure that its Employees are of legal working age and shall comply with local laws for youth employment or student work, such as internships or apprenticeships.
M-KOPA does not collect/charge any money as a pre-employment or post-employment requirement. This means that we never ask for ‘recruitment fees’, ‘processing fees’, ‘interview fees’, or any other kind of money in exchange for offer letters or interviews at any time during the hiring process.
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