Position purpose

• The Data and Analytics Lead at AfroCentric is a strategic leadership role responsible for shaping and executing the data and analytics strategy across the AfroCentric Group. The primary purpose of this role is to harness the power of analytics and machine learning to drive value creation through data-driven decision-making, enhance customer experiences, optimise operational efficiency, and support the achievement of strategic business objectives.
• The Data and Analytics Lead plays a crucial role in transforming AfroCentric into a data-centric organisation, ensuring that data is leveraged as a strategic asset and exploring emerging analytics technologies to maintain a competitive edge.

Key Performance Areas:

Data and Analytics Strategy:

• Implement and continuously improve the data and analytics strategy aligned to AfroCentric’s strategic goals, maintaining alignment with Sanlam Group initiatives and opportunities.

Advanced Analytics:

• Lead the development and deployment of advanced analytics, machine learning, and artificial intelligence solutions to extract valuable insights, improve risk management, and enhance customer engagement.

Business Intelligence:

• Drive the development of business intelligence solutions that provide actionable insights to various business units, enabling informed decision-making.

Data Product Management and Governance:

• Oversee the development of data products to ensure data has the quality, integrity and usability needed for users to launch straight into their work.
• Minimise the time spent by actuaries, data scientists and analysts preparing data to a state where it’s optimised for their use cases, ensuring availability and accessibility across the organisation.

Data Platform Strategy and Roadmap:

• Collaborate with data technology leaders to ensure the technologies we use, and the security and governance policies we have, are serving to empower value creation in the Business

Stakeholder Collaboration and management:

• Collaborate with business leaders, technology teams, and external partners to identify opportunities for data-driven innovation and support cross-functional initiatives.
• Manage group and internal stakeholders i.e., AfroCentric Technologies, Sanlam Corporate, Sanlam Life and Savings, and Sanlam Group Technology to deliver on business unit data requirements.

Risk Management:

• Manage the data-related risks and compliance with relevant regulations, ensuring data privacy and security.

Talent Management:

• Work with other Data Science and Analytics leaders across the wider Sanlam and AfroCentric groups to ensure we have the most competitive talent proposition, providing opportunities for high performers to gain experience working in different business units.
• Lead the conversation around calibration and facilitating internal mobility horizontally and vertically in data across the group.

Innovation and Value Creation:

• Identify and capitalize on opportunities to generate value from data, driving discovery and innovation in analytics, data science and AI to improve customer experiences, optimize operational efficiency and achieve commercial goals.

Position Metrics Include:

• Data-driven insights and recommendations that contribute to business growth, risk mitigation, and operational efficiency.
• Quantifiable business value.
• Well-defined data governance policies and procedures.
• Advanced analytics models and algorithms to enhance decision-making.
• Business intelligence dashboards and reports.

Qualification and Experience 

• Minimum 7 years’ experience in a senior leadership or executive role within data management and analytics.
• 5+ years in the financial services industry (health insurance is preferable)
• Experience with advanced analytics tools, machine learning, and AI methodologies is highly beneficial, reflecting the role’s commitment to innovation and value creation in data and analytics.

Qualification

• Master’s degree in computer science, data science, management information systems, statistics, analytics or related STEM field.

Knowledge and Skills 

Knowledge

• A deep understanding of health insurance, financial services and related regulatory environments.
• Knowledge of emerging trends and best practices in data and analytics.
• Familiarity with data privacy regulations (e.g., GDPR, POPIA, AML).
• Proficiency in data-related technologies and tools (e.g., data lakes, data warehouses, data visualization, machine learning).
• Business acumen and financial literacy.

Attributes

• Leadership: The ability to lead and inspire a large team to achieve data and analytics objectives.
• Data Strategy: Proficiency in developing and executing data strategies that align with business goals.
• Innovation and Strategic Vision: Ability to identify and integrate cutting-edge analytics technologies that drive strategic value and keep the organization at the forefront of data-driven innovations.
• Advanced Analytics: Strong knowledge of advanced analytics techniques and their application in a financial services or health insurance context. 
• Business Intelligence: Knowledge of Business Intelligence tool, frameworks and methodologies applied in a financial services or health insurance context.
• Balancing Commercial Acumen and Technical Judgement: Depth of understanding of the operations of the business, emerging risks coupled with relevant laws, rules, and regulations. A track record of leveraging expertise and judgment to create value.
• Navigating Organisational Scale and Complexity: Demonstrable experience of navigating a complex, highly matrixed organisation that comprises multiple business units and lines of business. Can demonstrate excellence in cross enterprise engagement, driving outcomes, delivering for their function and for the Group.
• Data Governance: Expertise in establishing and maintaining data governance frameworks.
• Stakeholder Engagement: Effective communication and collaboration with diverse stakeholders.
• Risk Management: Understanding of data-related risks and compliance requirements.
• Technology Proficiency: Familiarity with data technologies, analytics tools, and data management platforms, specifically with Data Lakehouse technologies and related architectures.