About this role
Who we are
Ranked in 2024 by the Financial Times, Moniepoint is Africa’s fastest-growing fintech, trusted by over 10 million business and individual accounts, processing billions of Naira in transactions monthly . Our mission is to enable financial happiness for every African, everywhere .
About this role:
We're looking for a Data Scientist to sit at the heart of how we fight fraud — building the models, experiments, and detection systems that protect millions of customers and merchants across our platform . This is a high-impact role at the intersection of machine learning, product, and engineering, where your work will directly shape how Moniepoint detects and responds to emerging fraud threats .
You are a data-driven, intellectually curious Data Scientist who is energized by hard problems in fraud and financial crime . You'll prototype and ship ML models, design experiments, and uncover new fraud signals across our ecosystem — partnering closely with engineers, product managers, and analysts to turn your work into production-grade systems .
Responsibilities:
• Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles .
• Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction .
• Size fraud typologies across our product lines to inform prioritization and investment decisions .
• Build and maintain anomaly detection systems to surface novel fraud vectors before they scale .
• Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations .
Experience & Background:
• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar).
• 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.
• Hands-on experience building and deploying machine learning models in a production environment.
• Fraud, risk, or financial services experience is a strong plus.
• Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering.
• Comfort working in fast-paced, cross-functional teams with high ownership expectations.
Skills & Competencies:
• Proficiency in Python and SQL; comfort working across the full model development lifecycle .
• An investigative instinct — you enjoy digging into data to find patterns others miss .
• The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action .
What Success Looks Like in This Role:
• Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale .
• Well-designed experiments that successfully balanc