Head: Data & AI at Old Mutual

Roles and Responsibilities

Strategy & Leadership

• Lead the definition, development, and execution of the enterprise Data strategy, incorporating analytics and AI, ensuring alignment with CGIC's business objectives and regulatory expectations.
• Provide strategic guidance and roadmaps for data investments and initiatives, focusing on building scalable analytics/AI delivery.
• Collaborate with senior leadership, EXCO/Board, and stakeholders to translate business requirements into effective data solutions, emphasizing value framing and measurable outcomes.
• Drive a culture of data-driven innovation, governance, and continuous improvement across the organization.

Portfolio & Architecture Governance

• Develop, implement, and maintain the Data portfolio strategy, including prioritized use-case portfolio delivering measurable value.
• Monitor data portfolio alignment to business strategy, ensuring consistency, data quality, ownership, access, and lineage in collaboration with IT and business units.
• Establish AI governance framework (risk, controls, model lifecycle) aligned to insurance regulatory expectations.
• Identify, manage, and resolve architecture deviations across the data lifecycle, aligning data platform and integration with IT (without direct ownership).

Technology Evaluation & Adoption

• Lead the identification, research, and evaluation of new data, analytics, and AI technologies, with a focus on cloud data platforms.
• Track and measure adoption rates, time-to-adopt, and impact of new data capabilities, ensuring they support insurance-specific needs like risk assessment and claims analytics.
• Ensure proactive scanning of industry trends, particularly in financial services/insurance, to leverage opportunities that enhance business objectives.

Performance, Value & ROI Measurement

• Track and measure Data portfolio ROI, financial benefits, and overall value delivery, with emphasis on first 6–12 months outcomes.

Key Outcomes for 6-12 months

• Data & AI strategy and operating model agreed and in execution
• Prioritised use-case portfolio delivering measurable value
• Data governance foundation (quality, ownership, access, lineage) in place in collaboration with IT and business
• AI governance framework (risk, controls, model lifecycle) aligned to regulatory expectations
• Team hired and operating (data engineering; data analytics / data science)
• Monitor time-to-market for new data initiatives and performance against delivery expectations.
• Provide regular updates and reports on initiatives, performance metrics, and portfolio progress to EXCO/Board/Group.

Security, Compliance & Risk Management

• Ensure data solutions adhere to security standards, regulatory requirements (e.g., data privacy, model risk in insurance), and industry best practices.
• Partner with risk/control teams to proactively identify, mitigate, and resolve data-related risks and vulnerabilities, with comfort in a risk/control environment.
• Develop and maintain strategies for data protection, disaster recovery, and business continuity, including governance policies, standards, controls, and documentation.

Stakeholder Engagement & Communication

• Serve as a strategic liaison between business leaders, IT teams, and stakeholders to ensure alignment and effective communication.
• Clearly articulate the value and benefits of data initiatives across the organisation, with strong business translation skills.
• Influence peers and operate under ambiguity, writing clearly to drive consensus.

Team Leadership & Development

• Lead, mentor, and coach data teams (engineering, analytics/science), hiring and developing talent to build a high-performing function.
• Manage cross-functional teams in the delivery of the portfolio, ensuring alignment with business imperatives, and partner with vendors where needed.
• Promote professional development, knowledge sharing, and certification within the team.
• Measure and drive employee engagement, satisfaction, productivity, and capability growth.

Qualifications & Experience

• Bachelor’s degree in Computer Science, Information Systems, Data Science/Analytics, Engineering, or a related field.
• Postgraduate qualification and/or a Master’s degree in a related field or MBA will be advantageous.
• 10+ years’ experience in data roles, with in-depth project management experience. Minimum 5 years leading data/analytics teams (delivery + stakeholders).
• Experience implementing governance (not just dashboards), including data quality, ownership, access, lineage, and AI/ML lifecycle.
• Strong business translation skills and comfort with risk/control environments.
• Insurance/financial services exposure is highly advantageous.
• Hands-on experience with enterprise applications and tools relevant to insurance (e.g., data platforms for risk modelling, claims data).
• Experience with data modelling, ETL pipeline development, and data governance frameworks.