Data Scientist (Fraud)

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: experimentatio