Data Scientist at FNB South Africa

Are You Someone Who Can

• Develop and implement machine learning models to detect and prevent fraud in payments
• Build and optimise real-time fraud scoring and decisioning systems
• Analyse and query large datasets to uncover fraud patterns and insights
• Use tools such as Python, SQL, Spark, and SAS to build scalable data solutions
• Work within and enhance fraud capabilities across payments platforms
• Continuously improve fraud detection accuracy while reducing false positives
• Collaborate across technology, operations, and product teams to deliver integrated solutions
• Contribute to the modernisation of payments ecosystems through data and AI
• Ensure compliance with regulatory, audit, and risk frameworks
• Deliver proactive, innovative solutions that improve customer outcomes and service delivery

You Will Be an Ideal Candidate If You

• Have 3–5 years’ experience in data science or AI role
• Have strong experience in machine learning applied to fraud detection
• Experience with machine learning frameworks (e.g., scikit‑learn, TensorFlow, PyTorch)
• Knowledge of fraud detection techniques (e.g., supervised/unsupervised learning, anomaly detection, graph analytics)

Are proficient in:

• Python for data science
• SQL for querying large transactional datasets
• Big data tools (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Analytics platforms (SAS Studio, SAS Enterprise Guide/Miner)
• Have experience working with real-time scoring or decisioning systems
• Have exposure to Kafka (or equivalent) plus model deployment frameworks
• Have exposure to core banking/payment systems
• Prior experience in banking, fintech, or payments industry is highly desirable
• Hold a degree in Data Science, Computer Science, Mathematics, Statistics, or similar

End Date: July 13, 2026