Data Culture Lead
Retail, Wholesale / FMCG
Minimum Requirements
- Bachelor's degree in data science, Information Systems, Business Analytics, Computer Science, or related field (essential).
- leading large-scale, cross-functional delivery initiatives with measurable outcomes.
- Strong execution discipline, planning capability, prioritisation skill and an outcome-oriented working style.
- Comfortable operating in ambiguity and driving delivery through complex organisational environments.
- Stakeholder & Influence
- Senior-level stakeholder engagement and influencing capability.
- Ability to challenge thinking constructively, drive alignment, and hold delivery accountability across functions.
- Credible, pragmatic, action-focused leadership style with sound judgement and professional maturity.
- Knowledge and Skills Exposure data governance, data management, and data quality principles (essential).
- Exposure advanced analytics techniques, AI-assisted tools (e.g., machine learning platforms, LLM-based features), and the practical adoption of AI in business processes (essential).
- Hands-on Job Objectives Strategy Translation & Focus Area Ownership
- Translate the Data Culture, Analytics and AI strategy into clearly defined priority focus areas with agreed outcomes, success measures and delivery horizons.
- Define and maintain execution of roadmaps aligned to enterprise priorities, organisational maturity objectives and measurable business value.
- Continuously assess organisational capability maturity to identify gaps, risks and opportunities across data, analytics and AI.
- Provide informed recommendations to the Head on priority trade-offs, sequencing and optimisation of initiatives to maximise value delivery.
- Data, Analytics & AI Culture Build
- Lead the design and delivery of integrated capability-building interventions spanning:
- Data literacy and decision-readiness
- Advanced analytics skills and analytical thinking
- Practical AI enablement and applied use cases
- Ensure capability initiatives are practical, scalable and outcome-focused, enabling measurable behavioural change and sustained adoption.
- Embed AI enablement into existing data and analytics frameworks, learning pathways and operating models.
- Translate complex technical concepts into relevant, accessible enablement tailored to varying maturity levels across the organisation.
- Programme & Delivery Leadership
- Take end-to-end accountability for delivery of multiple concurrent initiatives, including scope, timelines, quality standards and outcomes.
- Lead pilot initiatives, scale proven interventions, and discontinue or redesign initiatives that do not deliver value.
- Establish and maintain strong delivery cadence, governance structures, tracking mechanisms and reporting rhythms.
- Actively remove delivery impediments, resolve blockers, and maintain momentum to ensure execution at pace.
- Stakeholder & Change Leadership
- Engage senior leaders, product teams and business stakeholders to embed data, analytics and AI into ways of working.
- Build and enable a network of champions and practitioners across data, analytics and AI domains to support scale and sustainability.
- Influence across functions without direct authority, using insight, evidence, and outcomes to align decisions and behaviours.
- Ensure initiatives are owned, sustained, and embedded by the business rather than dependent on central enablement teams.
- Measurement, Insight & Continuous Improvement
- Define and track clear success metrics for all initiatives, including adoption, capability uplift, behavioural change and business impact.
- Use data, feedback, and insight to continuously refine delivery models, learning pathways, and enablement approaches.
- Provide concise, evidence-based reporting to the Head and senior stakeholders on progress, risks, decisions, and realised value.
- Decision-Making Authority
- Determine execution approaches, sequencing, and delivery models within the boundaries of the strategic direction, securing stakeholder alignment where required.
- Recommend changes to priorities, investment focus and course corrections based on delivery insight, maturity findings and business needs.
- Make day-to-day delivery, design and operational decisions within the defined scope and mandate of the role.
- Technical & Domain Expertise
- Advanced understanding of data and analytics value chains, analytical problem-solving patterns, and applied analytics use cases.
- Strong working knowledge of AI concepts applied AI techniques and responsible AI principles.
- Ability to connect technical capability and analytical/AI potential to real business-decision requirements and outcomes.
- Delivery & Leadership Capability
- Exposure data governance, data management, and data quality principles (essential).
Responsibilities
- Strategy Translation & Focus Area Ownership
- Translate the Data Culture, Analytics and AI strategy into clearly defined priority focus areas with agreed outcomes, success measures and delivery horizons.
- Define and maintain execution of roadmaps aligned to enterprise priorities, organisational maturity objectives and measurable business value.
- Continuously assess organisational capability maturity to identify gaps, risks and opportunities across data, analytics and AI.
- Provide informed recommendations to the Head on priority trade-offs, sequencing and optimisation of initiatives to maximise value delivery.
- Data, Analytics & AI Culture Build
- Lead the design and delivery of integrated capability-building interventions spanning:
- Data literacy and decision-readiness
- Advanced analytics skills and analytical thinking
- Practical AI enablement and applied use cases
- Ensure capability initiatives are practical, scalable and outcome-focused, enabling measurable behavioural change and sustained adoption.
- Embed AI enablement into existing data and analytics frameworks, learning pathways and operating models.
- Translate complex technical concepts into relevant, accessible enablement tailored to varying maturity levels across the organisation.
- Programme & Delivery Leadership
- Take end-to-end accountability for delivery of multiple concurrent initiatives, including scope, timelines, quality standards and outcomes.
- Lead pilot initiatives, scale proven interventions, and discontinue or redesign initiatives that do not deliver value.
- Establish and maintain strong delivery cadence, governance structures, tracking mechanisms and reporting rhythms.
- Actively remove delivery impediments, resolve blockers, and maintain momentum to ensure execution at pace.
- Stakeholder & Change Leadership
- Engage senior leaders, product teams and business stakeholders to embed data, analytics and AI into ways of working.
- Build and enable a network of champions and practitioners across data, analytics and AI domains to support scale and sustainability.
- Influence across functions without direct authority, using insight, evidence, and outcomes to align decisions and behaviours.
- Ensure initiatives are owned, sustained, and embedded by the business rather than dependent on central enablement teams.
- Measurement, Insight & Continuous Improvement
- Define and track clear success metrics for all initiatives, including adoption, capability uplift, behavioural change and business impact.
- Use data, feedback, and insight to continuously refine delivery models, learning pathways, and enablement approaches.
- Provide concise, evidence-based reporting to the Head and senior stakeholders on progress, risks, decisions, and realised value.
- Decision-Making Authority
- Determine execution approaches, sequencing, and delivery models within the boundaries of the strategic direction, securing stakeholder alignment where required.
- Recommend changes to priorities, investment focus and course corrections based on delivery insight, maturity findings and business needs.
- Make day-to-day delivery, design and operational decisions within the defined scope and mandate of the role.
- Technical & Domain Expertise
- Advanced understanding of data and analytics value chains, analytical problem-solving patterns, and applied analytics use cases.
- Strong working knowledge of AI concepts applied AI techniques and responsible AI principles.
- Ability to connect technical capability and analytical/AI potential to real business-decision requirements and outcomes.
- Delivery & Leadership Capability
