Job Description
Let's Write Africa's Story Together!
Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.
Key Result Areas
Forensic Intelligence and Fraud Analytics
- Development of fraud intelligence solutions.
- Identification of emerging fraud trends and typologies.
- Creation of proactive detection models.
- Development of fraud risk indicators and early warning systems.
- Production of intelligence products supporting investigations and risk management.
- Collaboration with Compliance, Risk, Internal Audit and Operational teams to identify emerging risks
Data Science – thought leadership
- Influence the adoption of AI in support of fraud management. You will be an advocate for the use of AI solutions in fraud management, providing research insights and strategic input.
Accountabilities
Data Science - Extend the use of applicable technologies, including, but not limited to:
- Graph analytics, knowledge graphs and link analysis,
- Entity resolution,
- AI-assisted investigations,
- Generative AI applications,
- Intelligent document review, and
- Automated evidence analysis.
Responsible for ensuring
- Ethical AI usage,
- Explainable models,
- Appropriate governance over automated decision making,
- Compliance with applicable regulatory requirements,
- Bias monitoring within machine learning models.
Solutions development and maintenance
- Development, support, and management of Forensic solutions,
- Maintenance and optimization of data-pipelines,
- Own the end-to-end MLOps lifecycle across AWS and DataBricks, applying a Feature / Training / Inference (FTI) pipeline architecture,
- Integration with business processes.
Provide architectural steer to GFS development team. Accountability for:
- Forensic technology roadmap development,
- Technology evaluation and vendor assessments,
- Architecture design for future forensic platforms,
- Integration with enterprise systems,
- Cloud strategy alignment,
- AI security and governance controls.
Provide support on strategic initiatives, including development of solutions towards:
- Intake automation, Case triage and Workflow orchestration,
- Case assignment logic,
- Investigation management solutions,
- Automation of reporting and MI.
Key Performance Indicators:
- Fraud losses prevented
- Value identified through analytics
- Number of fraud detection models deployed
- Reduction in manual effort,
- Improvement in case triage efficiency
- Increase in detection rates
- Reduction in false positives
- Number of automated controls implemented
- User adoption of forensic technology solutions
- Impact of solutions implemented
Qualifications, Skills, and Experience:
- Degree or diploma in Information Technology, Information Systems, Mathematics, Computer Science, or related disciplines. Master's degree advantageous.
- Strong Python and SQL, with deep proficiency in ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow, and Spark MLlib
- 8+ years of experience in data science and/or ML engineering,
- Strong working knowledge of AWS data and ML services (e.g., S3, Glue, Step Functions, SageMaker) and Databricks (Delta Lake, Unity Catalog, MLflow), with the ability to work fluently across both platforms.
Experienced in / with:
- Deep expertise in fraud/risk domain modelling
- Graph analytics and network-based fraud detection
- Building production MLOps pipelines following a Feature / Training / Inference (FTI) pattern
- Designing and operating production architectures that combine a batch backbone with real-time/near-real-time scoring layers
- Developing and packaging software solutions
- Working in AWS / Azure environments
- Enterprise IT environments,
- Fraud Risk Management
- AML
- Financial Crime
- Insurance Fraud
- Banking Fraud
- Internal Investigations
Knowledge and experience in any / all the following would be an advantage:
- Streaming technologies (e.g., Kafka, Amazon MSK, Apache Flink, or Spark Structured Streaming)
- Data / Systems integration (e.g., using APIs) experience
- Full Data Stack implementation using AWS, SQL, CloudFormation, GitHub
- Infrastructure-as-Code and MLOps tooling (e.g., CloudFormation, Terraform, GitHub Actions or similar CI pipelines)
- Experience using Alteryx, RStudio or Jupyter notebooks
- Experience with ML frameworks and tools (e.g. pandas, numpy, scikit-learn, TensorFlow, Pytorch, Spark MLlib)
- Entity resolution and identity-graph techniques — linking customers, devices, agents, and accounts across fragmented data sources
- Software containers (Docker/Kubernetes)
- Agile development methodology
Competencies:
- Strategy
- Innovation
- Leading with Influence
- Collaboration
- Customer First
- Execution
Personal Mastery
To lead the development and deployment of advanced analytics, machine learning, artificial intelligence and forensic technology solutions that enhance the Group's ability to proactively detect, prevent, investigate and manage financial crime risks. The role serves as a key enabler of the GFS transformation journey toward becoming an intelligence-led and technology-enabled forensic function
Skills
Action Planning, Business Requirements Analysis, Computer Literacy, Data Compilation, Data Controls, Data Management, Executing Plans, IT Architecture, IT Network Security, Policies & Procedures
Competencies
Business InsightCultivates InnovationDrives ResultsManages AmbiguityManages ComplexityPlans and AlignsSituational AdaptabilityStrategic Mindset
Education
NQF Level 7 - Degree, Advance Diploma or Postgraduate Certificate or equivalent
Closing Date
06 August 2026 , 23:59
The appointment will be made from the designated group in line with the Employment Equity Plan of Old Mutual South Africa and the specific business unit in question.
The Old Mutual Story!
