Senior Data Scientist (Credit) at Moniepoint Incorporated - Remote

Moniepoint Incorporated is a global business payments and banking platform and recently became QED Investors’ first investment in Africa. We are the partner of choice for over 600,000 businesses of all sizes, powering the dreams of SMBs and providing them with equal access to the tools they need to grow and scale.

We are recruiting to fill the position below:

Job Title: Senior Data Scientist (Credit)

Location: Remote

About the Role

• At Moniepoint, data is at the core of everything we do. We are a customer-centric company, and your work will enable our teams to make informed, data-driven decisions that directly impact the success of our business.
• We are seeking a data-driven, impact-oriented Senior Data Scientist to power Moniepoint’s consumer lending decisions at scale. You will build the models, experiments, and insights that shape how we acquire, underwrite, and retain millions of customers across Nigeria and Africa.
• This role is pivotal in driving our pricing, credit limit modelling, and credit model deployment strategy in a growing business with unparalleled opportunities to give access to millions of customers. 
• You’ll collaborate with our amazing Consumer Credit team and drive how we think about risk ranking, product offering, and data infrastructure to create the best personalised offers for our customers.
• You’ll be responsible for building our first suite of credit risk models and help drive a culture of how we should think about machine learning vs more traditional methods - bringing fresh thinking to inspire both the team and our customers.
• Sitting at the intersection of data science, credit risk, and product, you’ll translate complex data into actionable decisions that drive growth with healthy portfolio performance.

What You’ll Do

• Develop and deploy credit scoring, affordability, and behavioural models to support underwriting, pricing, and collections
• Design and run experiments to optimise approval rates, loss rates, and profitability
• Partner with product squads to embed decision logic into real-time systems
• Ensure data quality, compliance, and ethical use of models across all decisioning processes
• Mentor product squads on best practices in experimentation and data-driven decision making
• Provide models to optimise outcomes in collections, churn management and user retention

Requirements
We would love to hear from you if:

• You’re comfortable with Statistics - and have a Degree or qualifications  in a quantitative field (Statistics, Mathematics, Engineering or similar)
• You have +5 years of experience in data science, decision science, or risk analytics within financial services
• Working knowledge of credit risk, consumer lending, and regulatory considerations
• Proficiency in SQL and at least one modelling/programming language (Python, R)
• Experience with A/B testing, machine learning, collections modelling and churn management
• Ability to translate complex analyses into clear recommendations for business stakeholders
• High ownership mindset and comfort working in fast-paced, cross-functional teams.

What we can offer you

• Culture: We put our people first and prioritize the well-being of every team member. We’ve built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human.
• Learning: We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks.
• Compensation: You’ll receive an attractive salary, pension, health insurance, monthly bonuses, plus other benefits.

Application Closing Date
Not Specified.

Method of Application
Interested and qualified candidates should:
Click here to apply online

What to expect in the hiring process

• A preliminary phone call with the recruiter
• A coding exercise on HackerRank - covering core data science theory (math, statistics, linear algebra) and Python fundamentals (data structures & algorithms).
• A take-home assignment
• A technical interview with a Lead in our Data Science Team to review your take-home assignment in depth
• A behavioural and technical interview with the hiring manager.