Data Scientist (Fraud)

Job description

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; comf…