Senior Data Scientist - Credit Modeling

We are looking for a Senior Data Scientist with strong modeling experience to help drive M-KOPA’s mission of building transformative lifetime financial partnerships with our customers.

''This role offers the opportunity to directly impact millions of customers’ access to credit by building sophisticated credit scoring models and underwriting frameworks. You will be a member of a small team with the big responsibility of continuously improving M-KOPA’s loan offers and eligibility criteria through advanced predictive modeling 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’re building the future of financial inclusion, and data-driven decision making is at the heart of our mission. Our team combines analytical rigor with deep market understanding to develop loan eligibility and pricing for customers who have traditionally been excluded from formal financial services.

We foster a low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact. You’ll be empowered to make data-driven decisions and clear cases for prioritization of solutions in a domain that you have a high degree of ownership over.

You’ll be joining a team that’s rapidly expanding our credit and underwriting capabilities. We are looking for someone who loves building predictive models, analyzing complex data, and solving challenging, ambiguous data problems — if that sounds like you, you might be a fit!

In this role, you would be responsible for:

Building and refining credit scoring models to assess customer creditworthiness and default risk

Analyzing M-KOPA’s repayments data and other data sources to continuously improve our loan eligibility criteria while managing credit risk

Developing machine learning models for loan eligibility decisions and pricing optimization

Refining loan pricing based on credit analysis, predictive modeling, and customer behavior

Testing new types of loans to understand customer demand and credit performance through A/B testing and statistical analysis

Monitoring credit performance to detect risk shifts and quantify margin impact using advanced analytics

Testing the predictiveness of new data sets and feature engineering for enhanced model performance

Using Python, SQL, and other tools for data analysis and model development

Collaborating with data scientists to implement and scale machine learning models in production

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 of experience building predictive models, particularly credit scoring, risk models, or similar classification/regression problems

Strong machine learning background with experience in model development, validation, and deployment

Advanced statistical modeling and quantitative analysis skills, including experience with model evaluation metrics and performance monitoring

Proficiency in Python, SQL, and relevant ML libraries (scikit-learn, pandas, numpy, etc.)

Experience with feature engineering, model selection, and hyperparameter tuning

Experience translating complex model outputs into actionable business strategies and stakeholder communications

Ability to work cross-functionally with product, engineering, and commercial teams

Strong data communication skills — written, oral, and visual

Strong interpersonal and collaboration skills

(Highly desirable) Experience in credit, underwriting, lending analytics, or fintech modeling

Credit accessibility and affordability are at the core of this role. You’ll join a small, high‑performing team where every day brings new modeling challenges and analyses that shape our lending strategy. If building models that can transform financial access for millions of African customers excites you, we’d love to hear from you.

If the above is of interest to you, please apply.

Why M-KOPA?

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 four times by the Financial Times as one Africa's fastest growing companies (2022, 2023, 2024 and 2025) and by TIME100 Most influential companies in the world 2023 and 2024 , we've served over 6 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.

Applications for this position will be reviewed on a rolling basis. Shortlisting and interviews will take place at any stage during the recruitment process. We reserve the right to close the vacancy early if a suitable candidate is selected before the advertised closing date.

If your application is successful M-KOPA undertakes pre-employment background checks as part of its recruitment process, these include; criminal records, identification verification, academic qualifications, employment dates and employer references.