About the role:
We’re looking for a hands-on Senior Data Scientist (Fraud) to help detect, prevent, and reduce fraud across one of the largest financial transaction ecosystems in Africa. Operating at the heart of real-time payments, identity, behavioural risk, and transaction monitoring, this role works at massive scale with direct, real-world impact.
You’ll partner closely with Fraud, Risk, Product, and Engineering teams to design, build, and deploy production fraud models that sit directly in decision flows. Sitting at the intersection of data science, fraud strategy, and product, you’ll translate complex behavioural signals into high-confidence, real-time decisions - balancing fraud loss, customer experience, and regulatory expectations to protect millions of customers and businesses.
What You’ll Do
- Develop and deploy fraud detection, transaction monitoring, and behavioural risk models across payments, accounts, onboarding, and merchant activity
- Design and run experiments to optimise fraud catch rates, false positives, and customer friction
- Partner with product and engineering teams to embed models into real-time decisioning systems
- Build features from high-volume transactional, device, network, and behavioural data
- Continuously monitor model performance, drift, and emerging fraud patterns
- Ensure data quality, governance, and responsible use of models in regulated environments
- Support investigations, strategy, and policy teams with advanced fraud analytics
- Mentor analysts and product teams on experimentation, detection strategy, and data-driven decision making
We would love to hear from you if…
- A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar)
- 5+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime
- Hands-on experience with fraud detection, transaction monitoring, or behavioural risk modelling
- Proficiency in SQL and at least one modelling/programming language (Python or R)
- Experience with machine learning, anomaly detection, network/graph features, and real-time decision systems
- Strong intuition for fraud typologies, adversarial behaviour, and evolving attack patterns
- Ability to translate complex analysis into clear, actionable recommendations for technical and non-technical stakeholders
- High ownership mindset and comfort working in fast-paced, cross-functional product environments