Head of Data, AI & Analytics

Overview

Head of Data, AI & Analytics Jobs in City of Cape Town, Western Cape, South Africa at M&G Investments Southern Africa

Title: Head of Data, AI & Analytics

Company: M&G Investments Southern Africa

Location: City of Cape Town, Western Cape, South Africa

Overview

M&G aims to centralise and elevate its data, analytics, and AI capabilities by integrating data governance, BI/insights, advanced analytics, and AI lifecycle management under a single leadership role. The creation of this role supports the following objectives reporting to the COO:

• Build and maintain data infrastructure to provide a single source of clean, reliable and timely data for the whole business,
• Strengthen enterprise data governance and regulatory alignment (FSCA, POPIA, global AI/model risk standards).
• Unify siloed BI, analytics, data engineering and data management functions to deliver consistent, enterprise-wide reporting and insights.
• Establish responsible and well-governed AI capabilities, ensuring explainability, fairness, monitoring, and compliance.
• Improve operational efficiency and reduce duplication, using shared platforms, architectures, and delivery approaches.

Overall, the role ensures the organisation has trusted data, actionable insights, and responsible AI capabilities to support investment, distribution, operations, risk, compliance, and enterprise strategy.

Preference will be given to appropriately qualified, previously disadvantaged candidates, in accordance with M&G’s commitment to Employment Equity.

Key Responsibilities

Data Governance & Data Management

• Build, own and maintain the enterprise data governance framework.
• Define and enforce data standards, stewardship roles, metadata, lineage and data quality controls.
• Ensure compliance with POPIA, FSCA reporting, model risk, data privacy, and internal controls.

Business Intelligence & Enterprise Insights

• Lead the enterprise reporting and analytics function.
• Standardise BI tools, data models, dashboards, and reporting processes.
• Enable self-service analytics and ensure consistent insight delivery across domains.

Data Engineering & Architecture

• Develop, build, test and maintain data pipelines, databases, and data structures.
• Acquire, integrate, and prepare large datasets from on-premise and cloud sources (Azure Data Factory, Azure SQL, Azure Data Lake, Synapse, Databricks).
• Automate data ingestion and cleaning processes; optimise pipelines for reliability and efficiency.
• Design data environments that support analytics, reporting, ML, and AI.

Advanced Analytics, Data Science & AI Governance

• Oversee predictive modelling, ML/AI development, and advanced analytics.
• Own the AI/model lifecycle, documentation, and governance structures.
• Implement AI safeguards including monitoring, fairness, explainability, algorithmic risk controls.
• Partner with Risk, Compliance, Technology and business units on responsible AI adoption.

Strategic Leadership & Culture Enablement

• Build and lead a multidisciplinary team (Data Engineering, Data Governance, BI, Analytics, AI/ML).
• Align data & analytics roadmap to business strategy.
• Provide executive-level reporting on data risk, AI risk and analytics performance.
• Champion a data-first and responsible AI culture across the enterprise.

Qualifications, Skills & Experience

• Relevant degree (Honours or Masters) in Computer Science, Information Systems, Data Science, Statistics or a related field
• MBA an advantage
• 10+ years in data analytics, BI, data management, engineering, or AI/ML leadership roles.
• At least 5 years in data engineering within financial services.
• Experience working in Agile delivery environments (Scrum, Kanban).
• Proven ability to lead cross-functional teams.
• Expert-level knowledge of Azure data technologies:
• Azure Data Factory, Azure Data Lake, Azure SQL, Synapse, Databricks.
• Strong programming expertise: SQL, Python, ML/AI frameworks, BI tools (Power BI).
• Deep understanding of enterprise data governance, metadata, lineage, data quality, privacy and regulatory compliance.
• Strong understanding of AI governance, including fairness, explainability, monitoring, and model risk.
• Experience with financial services data, including trade, portfolio, market, client, operational and risk data.
• Understanding of regulatory reporting and conduct requirements (POPIA, FSCA).

Competencies & Personal Attributes

• Strong organisational skills and attention to detail.
• Clear and confident communication skills.
• A proactive, solutions focused mindset.
• Resilience and calm under pressure.
• A collaborative and professional approach.