At a Glance
- Tasks: Build predictive models that shape loan eligibility and pricing across Africa.
- Company: Join M-KOPA, a mission-driven company transforming financial access for millions.
- Benefits: Enjoy remote work, professional development, and family-friendly policies.
- Other info: Be part of a diverse team with excellent growth opportunities.
- Why this job: Make a real impact on financial inclusion while advancing your data science career.
- Qualifications: Experience in predictive modelling and proficiency in Python and SQL required.
The predicted salary is between 50000 - 70000 £ per year.
We're looking for a Data Scientist who loves building predictive models and solving ambiguous data problems. 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.
The Impact
- 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 is 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
- Building and refining credit scoring models 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.
- 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 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.
Our Mission
We make financing for everyday essentials accessible to everyone. We strive to drive greater inclusion of women, youth, and low-income communities.
Our Impact
- Connected: 2.5 million first-time smartphone users connected.
- Prosperous: 70% of customers use M-KOPA products for income generation, with 35,000 livelihoods created for agents.
- Green: 2.1 million tonnes of COâ‚‚ avoided through clean energy products, with over 127,700 circular economy products provided.
Ready to build models that create real-world financial inclusion while advancing your career in data science? Apply now.
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, prioritise well-being, and embrace flexibility.
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.
Data Scientist - Credit Eligibility in Birmingham employer: M Kopa
Contact Detail:
M Kopa Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Credit Eligibility in Birmingham
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for interviews by practising common data science questions and case studies. Get comfortable explaining your modelling process and how your work impacts business decisions. Remember, it’s all about showcasing your skills and passion!
✨Tip Number 3
Don’t just apply anywhere; focus on companies that align with your values and mission. At StudySmarter, we believe in making a difference, so look for roles where you can contribute to meaningful projects, like expanding financial access.
✨Tip Number 4
Follow up after interviews! A quick thank-you email can go a long way in keeping you top of mind. It shows your enthusiasm for the role and gives you another chance to highlight why you’re the perfect fit.
We think you need these skills to ace Data Scientist - Credit Eligibility in Birmingham
Some tips for your application 🫡
Show Your Passion for Data Science: When writing your application, let your enthusiasm for data science shine through! Talk about your love for building predictive models and how you enjoy tackling complex data problems. We want to see that you're not just qualified, but genuinely excited about the impact your work can have.
Tailor Your Experience: Make sure to highlight your relevant experience in credit scoring and risk models. Use specific examples of projects you've worked on that relate to the role. This helps us see how your background aligns with what we're looking for, so don’t hold back!
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language to explain your skills and experiences. We appreciate a well-structured application that makes it easy for us to understand your qualifications without wading through unnecessary fluff.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our mission-driven team at M-KOPA.
How to prepare for a job interview at M Kopa
✨Know Your Models Inside Out
Make sure you can discuss your experience with predictive models, especially in credit scoring and risk analytics. Be ready to explain how you've built, validated, and deployed these models, as well as any challenges you faced and how you overcame them.
✨Brush Up on Technical Skills
Since the role requires proficiency in Python, SQL, and relevant ML libraries, ensure you're comfortable discussing your technical skills. Prepare to answer questions about feature engineering, model selection, and hyperparameter tuning, and maybe even showcase a project or two.
✨Understand the Business Impact
Be prepared to translate complex model outputs into actionable business strategies. Think about how your work can directly impact lending strategies and customer access to credit, and be ready to share examples of how you've done this in the past.
✨Show Your Collaborative Spirit
This role involves working cross-functionally with various teams. Highlight your experience collaborating with engineers, analysts, and commercial stakeholders. Share specific examples of successful teamwork and how it led to better outcomes for projects.