Working on a team, you will determine M-KOPAs 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-KOPAs 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.
About Us
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.
Expertise
In this role, you would be responsible for:
- Analysing M-KOPAs 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
- 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.
