Data Enablement Lead (AI Solutions)

Job Summary

The Data Enablement Lead is accountable for turning data into business and operational outcomes across the organization. The remit is enablement: making sure the right data, defined and governed the right way, reaches the right decision or use case, and that business, operational, and technical teams trust and adopt the result.

The person in this role brings deep data technical fluency and a data science background but points that capability at value delivery rather than at building models or maintaining pipelines. They work at the seam between the business and the technical functions, translating ambiguous operational needs into clear data requirements, and translating data capability back into decisions teams can act on.

Within the AI Centre of Excellence, this role reports to the Group Head of AI Initiatives and works alongside the Forward-Deployed Engineer. Where the Forward-Deployed Engineer builds and ships solutions at the operational boundary, the Data Enablement owns the definition, governance, trust, and adoption of the data those solutions depend on. The two roles are designed to be complementary, not overlapping.

Key Responsibilities

• Own data outcomes, not data artefacts. Take business and operational priorities and convert them into defined, measurable data outcomes. Hold accountability for whether those outcomes are delivered, adopted, and trusted, rather than for the production of reports or dashboards in isolation.
• Translate between business and technical teams. Act as the credible bridge between operational and business stakeholders and the technical functions, including vendors and internal IT data science and engineering teams. Represent business constraints accurately to technical teams, and represent technical realities and trade-offs accurately to the business.
• Define and govern shared data meaning. Establish shared definitions, metric standards, and a common language for the data the organization relies on, so that the same question returns the same answer regardless of which team or system is asked.
• Assess and improve data readiness for AI. Evaluate whether data is fit for a given AI or analytics use case before work begins, identify the gaps, and direct the remediation needed to make use cases viable. Prevent AI initiatives from being deployed on data that cannot support them.
• Prioritize and sequence use cases. Work with leadership to identify, prioritize, and sequence data and AI use cases by value, feasibility, and risk. Maintain a clear, defensible view of where data effort should and should not be spent.
• Drive adoption and trust. Make sure the data products and AI outputs the Centre of Excellence delivers are understood, trusted, and used by the teams they are built for. Treat adoption as a core deliverable, not an afterthought.
• Build data capability across the organization. Raise the data fluency of business and operational teams so they can engage with data and AI tools with appropriate judgement, including knowing the limits of what the outputs can tell them.

Required Skills and Experience

Technical Proficiencies

• Advanced SQL, with the ability to query, join, and reason about data across multiple sources independently.
• Working fluency in a data science language, typically Python or R, sufficient to interrogate data, validate outputs, and engage with technical teams on method.
• Practical command of business intelligence and data visualization tools, such as Power BI.
• Familiarity with modern cloud data platforms, such as Snowflake, Databricks, Azure, or equivalent, at the level needed to understand what is feasible rather than to build or administer them.
• A sound understanding of data modelling, data quality, and governance concepts, and of how AI and machine learning outputs should and should not be relied upon.

Academic and Professional Qualifications

• A degree in a quantitative or data-oriented discipline, such as data science, statistics, mathematics, computer science, engineering, or a related field. Equivalent demonstrated experience will be considered in place of a specific degree.
• A postgraduate qualification in a relevant field is an advantage but is not required.
• Relevant professional certifications, for example in a cloud data platform, analytics tooling, or data governance, are welcome but secondary to demonstrated delivery.

What Success Looks Like

The right candidate will be comfortable moving between senior business conversations and detailed technical discussions. They will not just ask for better data; they will define what better data means, why it matters, what needs to change, and how the organization will use it.

This is a role for someone who can bring structure to ambiguity, build confidence in data, and help the organization make better decisions through disciplined, trusted, and usable data.