Senior Data Scientist - Credit Eligibility, Johannesburg

Join our Credit&Underwriting team! We're looking for a Senior Data Scientist who loves building machine learning models and solving ambiguous data problems. This is a fully remote data science role within a growing mission-driven fintech. You'll own the models that shape loan eligibility and pricing across 5 African markets. This is a small team with big responsibility, where your work directly shapes lending strategy for millions of customers.

Your models will directly shape how millions of underserved customers access credit for the first time. We've already helped over 7 million customers access over $2 billion in credit, and we process over 1.5 million payments daily. It's your chance to be part of something that's literally transforming lives across an entire continent.

The Opportunity

Mission-driven data science: Build credit scoring and pricing models that expand financial access for customers traditionally excluded from formal lending

Global recognition: Join a company named by TIME 100 as one of the world's most influential and by the Financial Times as Africa's fastest-growing for 4 consecutive years (2022–2025)

Scale challenges: Work with rich repayment datasets across 5 African markets, developing ML models that balance growth with credit risk at scale

Environmental impact: We're carbon-negative, having displaced over 2.1 million tonnes of emissions

What You'll Do At M-KOPA, you'll build and refine the machine learning and credit risk models that power our lending strategy. You'll sit within a small, high-performing team with end-to-end ownership of credit scoring, loan eligibility, and pricing optimisation— working cross-functionally with engineers, analysts, growth managers, and commercial stakeholders across multiple countries. Join us in combining cutting‑edge data science with purpose-driven work that makes digital and financial inclusion possible across Africa.

Day to day, you'll be:

Building and refining credit scoring and risk modelling solutions that assess customer creditworthiness, default risk, and loan pricing across multiple markets

Developing and testing ML models for loan eligibility and pricing optimisation through A/B testing and statistical analysis

Continuously improving eligibility criteria by analysing repayment data, engineering new features, and monitoring credit performance for risk shifts and margin impact

Collaborating cross-functionally with engineers, data scientists, and commercial stakeholders to scale models into production

Technical Environment

Languages&Libraries: Python, SQL, scikit-learn, pandas, numpy, and relevant ML libraries

Techniques: Predictive modelling, classification/regression, feature engineering, model selection, hyperparameter tuning, A/B testing

Domain: Credit scoring, underwriting, loan pricing, risk analytics

Our Team Approach

Low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact

High degree of ownership over your domain— you're empowered to make data-driven decisions and prioritise solutions

Cross-functional collaboration with engineering, product, and commercial teams across multiple countries

Analytical rigour combined with deep market understanding to serve customers excluded from formal financial services

What You Need Credit accessibility and affordability are at the core of this role. You'll join a small, high-performing team where every day brings new modelling 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.

Required Experience

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

Strong ML background with hands‑on experience in model development, validation, deployment, and performance monitoring

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

Highly Desirable

Experience in credit, underwriting, lending analytics, or fintech modelling

Location&Benefits

Fully remote Data Scientist role within UTC -1 to UTC +3 time zones

Work with diverse teams across UK, Europe, and Africa

Professional development programmes and coaching partnerships

Family-friendly policies and flexible working arrangements

Well‑being support and career growth opportunities

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.

Other standard applicant eligibility, background checks: 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.

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