Lead Data Scientist

Let's Write Africa's Story Together!

Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.

Job Description

The Lead Data Scientist is responsible for leading the development and implementation of advanced analytics, AI, machine learning, and forensic technology capabilities to strengthen the Group's approach to financial crime risk management. The role drives the transformation of Group Forensic Services into an intelligence-led, technology-enabled function, delivering data-driven insights and innovative solutions that enhance the detection, prevention, and investigation of financial crime.

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

 

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 Insight

Cultivates Innovation

Drives Results

Manages Ambiguity

Manages Complexity

Plans and Aligns

Situational Adaptability

Strategic 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!