Data Science Manager (Fraud)

6+ years of experience in Data Science, Decision Science, or Fraud Risk, ideally within financial services.

A degree or equivalent experience in a quantitative field (Statistics, Mathematics, Engineering, or similar).

Deep knowledge of fraud typologies, financial crime, and the regulatory landscape.

Proficiency in SQL and at least one programming language (Python is our primary tool).

Demonstrated success in building and deploying machine learning models at scale.

Experience leading or mentoring technical teams in a fast-paced environment.

The Data Science Manager (Fraud) leads our fraud decisioning efforts to ensure Moniepoint remains the most trusted financial partner for our users.

In this role, you’ll own the vision and execution of our fraud detection and prevention models. You aren’t just building algorithms; you’re making real-time decisions that protect millions of customers. You’ll lead a team of specialists to create innovative solutions for customer screening, transaction monitoring, and authentication, ensuring our growth is backed by world-class security.

Set the Strategy: Build and scale the roadmap for fraud decisioning, ensuring our models stay ahead of emerging typologies.

Lead the Team: Mentor and develop a team of data scientists, fostering a culture of mastery and continuous learning.

Architect Solutions: Partner with Product and Engineering to design and deploy real-time decision logic and data architectures.

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, solving real, meaningful problems at scale.