Senior Data Scientist (Fraud)

Moniepoint is a global fintech building modern financial services for millions of people and businesses across high-growth markets. We provide payments, banking, credit, and financial management tools - reliable products that people and businesses use every day to run their lives, grow their companies, and move money safely.

Our mission is simple: to enable financial happiness for every African, everywhere. And this is day one. We’ve grown rapidly in Nigeria and the UK, and we’re now expanding our product, engineering, and analytics teams. Our work ranges from building financial infrastructure to designing intuitive customer experiences for emerging markets - solving real, meaningful problems at scale.

We onboard over a million new customers each month, process 100s of billions of dollars in payments annually, and support tens of millions of users across our ecosystem. Our teams operate in a fast-paced, high-impact environment alongside international leaders from companies like Glovo, Bolt, Monzo, Klarna, Checkout.com, and Tide. If you want to build our data science function from the ground up, work with one of Africa’s highest-scale fintech data sets, and ship products that influence tens of millions of users, this is an exceptional time to join.

We’re looking for a hands-on Senior Data Scientist (Fraud) to help detect, prevent, and reduce fraud across one of the largest financial transaction ecosystems in Africa. Operating at the heart of real-time payments, identity, behavioural risk, and transaction monitoring, this role works at massive scale with direct, real-world impact.

You’ll partner closely with Fraud, Risk, Product, and Engineering teams to design, build, and deploy production fraud models that sit directly in decision flows. Sitting at the intersection of data science, fraud strategy, and product, you’ll translate complex behavioural signals into high-confidence, real-time decisions - balancing fraud loss, customer experience, and regulatory expectations to protect millions of customers and businesses.

Curious about what makes Moniepoint an incredible place to work? Check out posts on how we cultivate a culture of innovation, teamwork, and growth.

• →Develop and deploy fraud detection, transaction monitoring, and behavioural risk models across payments, accounts, onboarding, and merchant activity
• →Design and run experiments to optimise fraud catch rates, false positives, and customer friction
• →Partner with product and engineering teams to embed models into real-time decisioning systems
• →Build features from high-volume transactional, device, network, and behavioural data
• →Continuously monitor model performance, drift, and emerging fraud patterns
• →Ensure data quality, governance, and responsible use of models in regulated environments
• →Support investigations, strategy, and policy teams with advanced fraud analytics
• →Mentor analysts and product teams on experimentation, detection strategy, and data-driven decision making

• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar)
• 5+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime
• Hands-on experience with fraud detection, transaction monitoring, or behavioural risk modelling
• Proficiency in SQL and at least one modelling/programming language (Python or R)
• Experience with machine learning, anomaly detection, network/graph features, and real-time decision systems
• Strong intuition for fraud typologies, adversarial behaviour, and evolving attack patterns
• Ability to translate complex analysis into clear, actionable recommendations for technical and non-technical stakeholders
• High ownership mindset and comfort working in fast-paced, cross-functional product environments

• 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

• 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

Moniepoint is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and candidates.