Data Culture Lead

Purpose of the Job:

The Data Culture Lead is accountable for shaping, owning and delivering enterprise scale data and AI culture build initiatives across data, advanced analytics and AI, aligned to the strategic direction set by the Head of Data Culture. The role translates strategy into defined focus areas, executable roadmaps and measurable outcomes, and is responsible for end-to-end delivery, adoption and impact. The job holder operates with a high degree of autonomy, exercises professional judgement, and is accountable for ensuring that initiatives move from concept through to sustained business value. This role requires deep capability understanding across data, advanced analytics and AI, combined with strong execution discipline and a bias for action.

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 Proven experience 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.

Qualifications:

• Bachelor's degree in data science, Information Systems, Business Analytics, Computer Science, or related field (essential).

Experience:

• +5 years’ experience in data enablement, data literacy, analytics, digital learning, organisational change or similar role (essential).
• Experience executing data culture or digital adoption programmes (essential).

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 experience with data tools, visualisation platforms, and digital learning platforms (essential).