Data Science Manager (Fraud)

Who we are

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.

Job Purpose

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.

Key Responsibilities

• 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.
• Model Mastery: Develop fraud scoring methodologies and build features from high-volume transactional, device, and behavioural data.
• Optimise Outcomes: Design and run experiments to improve precision and recall, balancing security with a seamless user experience.
• Governance & Integrity: Ensure data quality and the responsible use of models within our regulated environments.
• Continuous Monitoring: Keep a pulse on model performance and drift, ensuring our defences evolve as fast as the threats do.

Qualifications

• 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.
• Preferred Qualifications
• Experience with A/B testing and experimentation in real-time environments.
• Familiarity with churn management and user retention modelling.
• Advanced degree (MSc or PhD) in a related quantitative field.

About you

• You’re a systems thinker: You see the patterns in data and know how to turn them into scalable defences.
• You have grit: You’re comfortable navigating the ambiguity of a high-growth scale-up and driving momentum through continuous improvement.
• You value mastery: You’re deeply technical but can translate complex analyses into clear, actionable stories for any stakeholder.
• You’re human-centric: You understand that behind every data point is a person’s financial happiness, and you’re driven to protect it.

What Success Looks Like

• Reliable Defences: Our fraud detection systems are highly accurate, maintaining a world-class balance between precision and recall.
• Team Growth: Your team feels supported, clear on their progression, and empowered to do their best work.
• Seamless Integration: Fraud decision logic is embedded into our products without creating unnecessary friction for our honest users.
• Data Integrity: Our models are well-documented, compliant, and consistently monitored for performance and bias.

What we can offer you

• Culture: We put our people first. We’ve built a company where all voices are heard and where we value each other as humans.
• Learning: We’re a knowledge-sharing environment with regular technical talks and a focus on training.
• Compensation: You’ll receive an attractive salary, pension, health insurance, annual bonus, and other benefits.

What to expect in the hiring process

• Recruiter Call: A preliminary chat with one of our recruiters.
• Take-home Task: A SQL and anomaly detection modelling case study.
• Task Review: A session with our Engineering Manager and Head of Data Science.
• Technical Interview: A deep dive with the Head of Data Science.
• Behavioural Interview: A conversation with our VP of Engineering and a Business Leader.