Data Science & AI Enablement Executive

Job Title: Data Science & AI Enablement Executive Reporting Line: Chief Information Officer Level: D21 4. JOB PURPOSE To lead CyberPro Consulting's Data, Analytics, and AI capability as a commercial value driver, delivering measurable improvements in revenue, margin, cost efficiency, and working capital through data-driven decision-making and AI-enabled solutions across Retail, Supply Chain, and Shared Services. The role ensures that data is treated as a strategic asset across the organisation, driving omnichannel growth, customer experience, operational excellence, and innovation, while embedding robust data governance, quality, integrity, and responsible AI practices across all business domains. The role is accountable for translating business priorities into production-grade data and AI solutions, ensuring adoption, scalability, and measurable business impact. 5. JOB OBJECTIVES The key responsibilities of the role include: Define and execute the Group Data & AI strategy, aligned with business priorities and delivering measurable outcomes across revenue growth, cost optimisation, risk reduction, and customer value. Translate strategic business challenges into data-driven solutions, leveraging advanced analytics, machine learning, and AI to enable predictive, prescriptive, and automated decision-making. Provide executive leadership with high-quality insights, including forecasting, scenario modelling, and decision-support tools to enable proactive, data-led decisions. Establish and enforce enterprise-wide data governance and master data standards, ensuring a single source of truth across all systems and business units. Partner with Enterprise Architecture, IT, and business leadership to enable scalable, secure, and integrated data and AI platforms and pipelines. Embed responsible and ethical AI practices, ensuring compliance, transparency, and effective risk management across all data and AI initiatives. Drive commercial, operational, and customer optimisation, including supply chain efficiency, personalisation, and performance improvement through advanced analytics. Build and lead a high-performing data and AI function, fostering critical capabilities across data science, analytics, and master data management. Drive innovation through the evaluation and adoption of emerging technologies, ensuring all investments are value-driven, scalable, and aligned with organisational strategy. 6. KEY DELIVERABLES Own and govern a prioritised portfolio of high-impact data and AI use cases, aligned with strategic objectives and business value (e.g. demand forecasting, pricing optimisation, customer analytics, and supply chain optimisation). Lead end-to-end delivery of analytics and AI solutions, from problem definition and business case through to deployment, adoption, and value realisation within operations. Define, track, and realise financial and operational value, including revenue uplift, margin improvement, cost reduction, and working capital optimisation. Embed data-driven decision-making into core business processes, in partnership with executive and business unit leadership. Ensure alignment with Enterprise Architecture and IT ecosystems, delivering scalable, integrated, and sustainable data and AI solutions. Establish and scale the organisation's data and AI operating model, including tooling, standards, governance frameworks, and best practices. Lead and manage a multidisciplinary function, encompassing data science, advanced analytics, and master data management. Oversee the industrialisation of models, ensuring robust deployment, monitoring, and lifecycle management through MLOps. Drive data quality, integrity, and governance, ensuring a strong and reliable data foundation across the enterprise. Ensure full compliance with data privacy and regulatory requirements, including POPIA, while embedding responsible AI practices. 7. JOB-RELATED KNOWLEDGE Essential Advanced knowledge of data science methodologies, machine learning, AI frameworks, and statistical modelling. Deep understanding of enterprise data platforms, data engineering, analytics tools, and cloud-based AI environments. Strong knowledge of data governance, Master Data Management (MDM), data quality, and data integrity frameworks. Knowledge of predictive analytics, forecasting, optimisation techniques, and decision science. Strong understanding of customer, retail, supply chain, and operational data ecosystems. Knowledge of AI ethics, responsible AI, and regulatory considerations. Desirable Retail (including omnichannel), FMCG, healthcare, or pharmacy analytics experience. MLOps, model lifecycle management, and AI platform scaling. Experience with personalisation, recommendation engines, and loyalty analytics. Understanding of enterprise architecture and system integration principles. Exposure to ERP, POS, supply chain systems, and customer platforms. ESG-aligned data and AI practices. 8. JOB-RELATED SKILLS Essential Strategic thinking and the ability to translate data into business value and measurable outcomes. Ability to translate complex business problems into scalable data and AI solutions. Leadership of multidisciplinary data teams, including data science, master data, and data quality. Advanced analytical problem-solving and hypothesis-driven modelling. Data visualisation and storytelling for executive audiences. Change management and adoption of data-driven decision-making. Vendor and partner management within AI and analytics ecosystems. Innovation and experimentation in AI and advanced analytics. Desirable Data visualisation and dashboarding capability. Executive communication, influencing, and data storytelling. Strong technical capability in Python, R, SQL, and cloud AI/analytics platforms. Model deployment, monitoring, and lifecycle management. Data governance, master data management, and quality assurance leadership. Stakeholder engagement and influencing skills at executive level. Ability to drive execution and ensure delivery into operational environments. Cloud data platform modernisation expertise. Cross-functional collaboration across IT, architecture, and business domains. 9. JOB EXPERIENCE Essential 12+ years' experience in data science, AI, analytics, or master data management. Proven experience deploying predictive and prescriptive models into production environments. Experience leading enterprise data science and master data teams. Demonstrated experience driving data and AI strategy across multiple business functions. Experience providing executive-level insights to support strategic and operational decision-making. Experience working in complex, data-rich environments. Strong leadership and people development track record. Desirable Retail, healthcare, or omnichannel business analytics experience. Experience in AI ethics, governance, and responsible deployment. Experience integrating AI solutions with enterprise architecture and IT platforms. 10. EDUCATION Essential Postgraduate degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related discipline. Desirable Business or commercial qualification (MBA). PhD in a quantitative or computational field. AI, machine learning, or analytics certifications. 11. JOB-RELATED COMPETENCIES The following competencies are critical for success in this role: Leading and Deciding Strategic leadership and decision-making. Taking ownership and accountability. Driving innovation and organisational change. Supporting and Cooperating Building collaborative relationships. Developing and empowering teams. Encouraging knowledge sharing and teamwork. Interacting and Presenting Executive stakeholder engagement. Influencing and negotiation. Communicating complex technical concepts clearly. Analysing and Interpreting Critical thinking and analytical reasoning. Data-driven problem-solving. Strategic interpretation of business intelligence. Creating and Conceptualising Innovation and forward-thinking. Developing AI-driven solutions. Identifying opportunities for business transformation. Organising and Executing Strategic planning and execution. Programme and portfolio management. Delivering measurable business outcomes. Adapting and Coping Managing ambiguity and complexity. Embracing technological change. Demonstrating resilience and adaptability. Enterprising and Performing Commercial awareness. Driving business value and return on investment. Delivering results through technology and innovation.