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Old Mutual is a firm believer in the African opportunity and our diverse talent reflects this.
Job Description
The Head of Data is a strategic senior management role responsible for creating enterprise value from the organization's data and analytics assets and ecosystem, building and running the data and AI foundation of the central ecosystem team including the governed data environment, customer intelligence, AI and recommender capabilities that allow banking, insurance, savings and rewards propositions to work together for customers. This position leads data strategy for the central team, governance and analytics capabilities to enable informed decision-making across the organization and drive competitive advantage. This includes engineering, data science, AI, analytics and governance, and decides which capability is needed, and when, to deliver the ecosystem objectives. The role bridges business and technology, partnering with executive leadership to align data initiatives with the ecosystem objectives while managing risk, ensuring compliance (including RDARR and POPIA) and fostering a data-driven culture. The role also scans emerging technologies, including generative and agentic AI and quantum computing, and decides when they are ready to be applied
Primary Purpose
You will establish and execute a comprehensive data, analytics and AI strategy that balances defensive imperatives (governance, compliance, risk management, data quality) with offensive opportunities (revenue growth, innovation, AI-enabled decision-making). You will also design and build the data environment and the AI and recommender capabilities that deliver the ecosystem’s propositions, deciding which engineering, data science and analytics skills are needed at each stage, and keep the ecosystem ready for emerging technologies such as agentic AI and quantum computing, adopting them only where there is a clear value case. You will serve as a strategic business leader, not solely a technology manager, accountable for ensuring that data and analytics investments deliver measurable business value while maintaining the trust, security and integrity of customers’ and the Group’s data assets.
Key Responsibilities
Ecosystem Data Platform & Architecture
- Design, stand up and run the ecosystem data space on Databricks (or an equivalent Group-approved lakehouse platform): workspaces, catalogues, environments, access controls and cost management, built on Group central data platforms rather than duplicating them
- Define the ecosystem data model and the fields required to deliver each project, with source-to-target mapping across OM Bank and the life, short-term insurance, retirement and rewards businesses, and a named data owner for every field
- Build the customer identity resolution and single customer view needed across businesses (matching, consent and golden record), within privacy and legal constraints
- Design the data pipelines and integration architecture (batch, streaming and APIs) with Technology and Architecture, and agree service levels with source system owners
- Establish data products, a feature store, vector and retrieval capabilities for AI, a data catalogue and lineage so that data can be discovered, trusted and reused across projects
- Make build, buy and partner decisions on platform components within Group architecture and procurement standards, and manage platform cost and performance
Data & Analytics Strategy & Business Value Creation
- Develop and communicate a clear data and analytics strategy that aligns with the ecosystem objectives (lending, deposits, bank-acquired funeral policies, VNB and new-to-group customers), articulating how data initiatives will drive revenue growth, cost reduction, risk mitigation and better customer outcomes
- Define the offensive and defensive mandates for data and analytics balancing strategic innovation (new products, services, market opportunities) with essential governance and compliance requirements
- Establish and track metrics that measure the business impact of data and analytics investments, connecting data initiatives directly to financial and operational KPIs.
- Lead the translation of executive business objectives into actionable data roadmaps for each ecosystem project, sequenced to project delivery dates, ensuring alignment between data capabilities and strategic priorities
Connected Intelligence: Recommender Engines & Advanced Analytics
- Own delivery of the connected intelligence capability: predictive models, next-best-action recommendations and recommender engines embedded in customer engagement channels, from use-case definition through to production
- Determine the best channel and timing for each offer (digital, WhatsApp, branch, adviser) and build the decisioning logic that supports it
- Set MLOps standards (model registry, monitoring, drift, champion-challenger and retraining) and ensure independent validation with Group model risk
- Run test-and-control experiments with Value and Commercials to demonstrate measurable uplift in acquisition, conversion, cross-sell and retention
- Work with the project teams, Rewards and Consolidations to turn data into propositions, for example identifying customers who would benefit from consolidation, annuity-to-bank or bundled offers
Data Governance & Operating Model
- Design and oversee the data governance operating model for the ecosystem data space, aligned to Group data governance, defining roles, accountabilities, and processes for managing data as a strategic enterprise asset
- Represent the ecosystem in Group data governance structures, setting policy for the ecosystem data space and ensuring appropriate behavior in the valuation, creation, consumption and control of data and analytics
- Establish governance structures aligned with organizational maturity and decentralization, working with distributed data stewards and business units to embed governance into daily workflows rather than treating it as ad hoc or IT-only
- Oversee data quality frameworks, including validation, lineage, cleansing, and monitoring, ensuring data trustworthiness and suitability for advanced analytics and AI applications, to RDARR standard
- Define and enforce policies for data access, security, privacy, and regulatory compliance, ensuring alignment with financial services regulations (e.g., POPIA, RDARR, King IV, regulatory reporting requirements)
- Own RDARR compliance for the ecosystem data: lineage, completeness, accuracy, timeliness and adaptability of the risk data used by OM Bank, with the data architecture, ownership and controls expected under BCBS 239 principles, working with Risk, Finance and Compliance
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AI Strategy & Readiness
- Lead the ecosystem’s AI and machine learning strategy and readiness, positioning data and analytics as the critical foundation for responsible, ethical AI deployment
- Establish or adopt, with Group AI governance structures, governance frameworks for AI use cases, defining approval processes, risk assessments and guardrails for generative AI, machine learning and advanced analytics
- Oversee the identification, prioritization and piloting of high-impact AI and analytics use cases, ensuring value-driven implementation and measurable ROI
- Set the ecosystem’s AI strategy beyond predictive models: where generative AI and AI agents can improve customer engagement, servicing, adviser productivity, operations and the central team’s own work, each with a value case and risk assessment
- Build the AI platform and engineering foundations: model and prompt management, retrieval grounded on governed ecosystem data, evaluation and testing, monitoring, cost control and human-in-the-loop safeguards
- Manage AI risk: bias and fairness, explainability, treating customers fairly, hallucination and data leakage controls, and third-party model and vendor risk, in line with Group model risk and AI governance, and with Legal Enablement on automated decision-making obligations under POPIA
- Report AI use cases, risks, performance and value to the executive and Group AI governance forums
Emerging Technology: Quantum Computing
- Scan emerging technologies, including quantum computing, and assess their relevance to the ecosystem (for example optimisation, simulation and risk problems), with an honest view of maturity, cost and timing
- Lead quantum-safe readiness for ecosystem data with Information Security, Architecture and Group Technology: an inventory of the cryptography in use, the exposure of long-lived customer data and a post-quantum migration path aligned to Group and regulatory expectations
- Run small, time-boxed proofs of concept with partners only where there is a clear value case and within existing budgets, and report results and recommendations to the executive and Group Technology
- Build relationships with technology partners, universities and industry forums to keep the ecosystem informed on AI and quantum developments
Cross-Functional Leadership & Stakeholder Engagement
- Partner actively with the Chief Financial Officer, Chief Risk Officer, Chief Information Officer, Chief Marketing Officer, Group Treasury, Group Technology and Architecture and business unit leaders to align data initiatives with enterprise strategy and ensure resource alignment
- Lead or participate in enterprise governance councils, communicating data priorities, progress and emerging challenges in executive-ready formats
- Facilitate change management for governance adoption, using targeted communication, incentive alignment and designated change agents to increase buy-in across the organization
- Conduct regular stakeholder engagement (stay conversations) to understand business needs, emerging data challenges and alignment gaps; translate feedback into strategy adjustments
- Agree capacity commitments for data engineering, architecture and platform support with Group Technology and the participating businesses, and have them embedded in sponsors’ scorecards so that named people are ring-fenced for the ecosystem
Technology & Analytics Platform Leadership
- Evaluate, recommend and oversee adoption of data management, analytics and AI technologies that enable the organization's data strategy
- Establish requirements for modern data platforms, including data integration, profiling, quality monitoring and AI-enabled automation tools, feature stores, MLOps and LLMOps, decisioning and experimentation capabilities
- Work with IT and technology partners to design scalable, secure and compliant data infrastructure that supports both near-term analytics needs and long-term AI ambitions
- Champion responsible adoption of advanced technologies including AI, machine learning and generative AI within governance frameworks that mitigate risk and ensure ethical deployment
Required Experience & Qualifications
Experience
- Minimum 10+ years in data engineering, data science, analytics or information management roles, with at least 5+ years at senior management level
- Demonstrated experience designing and implementing data governance frameworks, ideally in a financial services, insurance or regulated industry environment
- Proven success leading data strategy and transformation initiatives across multiple business units or geographies
- Experience managing the budget and external resourcing of a data and analytics function; demonstrated ROI on data investments
- Proven experience delivering production machine learning, recommender or next-best-action capabilities in a financial services or similarly regulated environment, with AI governance and model risk controls
- Working knowledge of regulatory environments in financial services (POPIA, RDARR, King IV, anti-money laundering, regulatory reporting) or equivalent regulated industry compliance
- Experience designing, building and running a cloud lakehouse platform (for example Databricks) from design to production, including environment, access and cost management
- Experience delivering generative AI or large language model solutions into production, with evaluation, monitoring and governance controls
- Experience owning RDARR or BCBS 239 (or equivalent regulatory data) compliance
Qualifications & Skills
- Post-graduate degree (PhD preferred) in mathematics, statistics, computer science, data science or a related quantitative field (essential)
- Advanced proficiency in data governance tools, analytics platforms and modern data stack technologies (including Databricks and Spark)
- Understanding of quantum computing and post-quantum cryptography developments, and their implications for data security and analytics, is an advantage
- Certification in data governance, data management (DAMA, IAIDPM) or business analytics preferred
- Strong financial acumen and comfort interpreting P&L statements, budgets, and ROI analyses
- Ability to operate effectively in ambiguity, manage competing priorities, and adapt strategy based on changing business context
- Proven openness to diverse perspectives, collaborative problem-solving mindset and comfort with debate and constructive conflict
- Written and verbal communication excellence; comfort presenting to executive audiences and translating technical concepts for business stakeholders
Skills
Adaptive Mindset, Business Intelligence (BI) Analysis, Business Requirements Analysis, Categorizing Data, Change Management, Classification Analysis, Data Algorithms, Database User Interfaces and Queries, Data Collection Methods, Knowledge Management, Planning and organisational skills, Prioritization, Project Risk and Issue Management, Stakeholder Expectation Management, Strategic Project and Program Alignment
Competencies
Balances Stakeholders
Business Insight
Collaborates
Communicates Effectively
Courage
Cultivates Innovation
Customer Focus
Decision Quality
Education
NQF Level 9 – Masters (Required)
Closing Date
21 November 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.
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