About the PRI

The PRI is the world’s leading proponent of responsible investment. It works to understand the investment implications of environmental, social and governance (ESG) factors and to support its international network of investor signatories in incorporating these factors into their investment and ownership decisions.

The PRI’s three distinct capabilities relate to the core elements of the PRI’s approach to achieving a sustainable financial system.

• Translate RI (Responsible Investment) thought leadership into insights and practical support that is tailored to what signatories need to progress their RI practice

• Convene our vast network to create opportunities for collaborative action

• Harness our global scale to influence policymakers and regulators to effect system change

Job Description

The Head of Data & AI Value is responsible for ensuring the organisation’s data assets are trusted, governed, understood, and actively used to deliver business value.  This is both a strategic and hands on role.

The role acts as the bridge between business teams, product teams, technology functions and external stakeholders, ensuring data is treated as a strategic asset while delivering practical outcomes. This includes improving data quality, defining critical data models and business journeys, enabling AI-ready data practices, supporting responsible use of structured and unstructured data, and identifying opportunities to monetise and create value from data assets alongside our product team.

The role is not a deep technical engineering or analytics position. Instead, it combines business leadership, governance, process simplification thinking, commercial awareness and strong organisational change to maximise the value of data across the organisation.

Core Responsibilities

Data Value & Business Ownership

• Define and maintain the organisation’s critical data domains, business data models and end-to-end data journeys.

• Work with business leaders to identify where data creates measurable value, improves decision-making and supports operational effectiveness.

• Establish clear ownership and accountability for critical data assets across business functions.

• Ensure data initiatives are prioritised based on business outcomes rather than technology-led activity or ‘great ideas’.

• Translate business objectives into practical data improvement programmes.

Data Quality & Assurance

• Establish and operate a pragmatic data quality framework focused on the data that matters most.

• Define critical data quality measures, standards and reporting.

• Conduct regular assurance reviews of key data sets, business processes and controls.

• Monitor data quality trends and work with business owners to address root causes.

• Ensure data risks are identified, tracked and mitigated through practical actions.

• Report regularly to leadership on data health, business impacts and improvement progress.

AI Readiness & Responsible Use of Data

• Develop organisational understanding of how data underpins AI capability and outcomes.  In 2026/2027 responsible for the AI Enablement and Adoption initiative to drive AI MS products through our organisation.

• Establish policies and guidance for the responsible use of structured and unstructured data within AI solutions.

• Work closely with technology, legal, risk and business teams to ensure appropriate governance of AI-related data usage.

• Identify opportunities to improve data accessibility and usability for AI-enabled products and services.

• Support evaluation of emerging AI use cases and ensure data readiness requirements are understood before implementation.

• Promote responsible handling of confidential, proprietary and sensitive information within AI environments.

• Responsible for AI education, engagement across business, upskilling and empowering our business teams in use of AI, driving value driven use cases across the organization and leading the AI & Data Champions network actively.

Unstructured Data Management

• Develop approaches for managing and extracting value from documents, research, publications, reports, learning content and other unstructured information assets.

• Define standards for classification, ownership, retention and accessibility of unstructured information.

• Work with business teams to improve discoverability and reuse of knowledge assets.

• Identify opportunities where AI can unlock value from previously underutilised information sources.

Data Product & Commercial Value

• Develop a product-oriented approach to organisational data assets.

• Work closely with product team to assess opportunities for data-driven revenue generation.

• Explore opportunities to create enhanced services for our signatories.

• Support product team business case development and investment decisions for data-enabled products.

Data Governance & Policy

• Establish pragmatic and effective data governance frameworks that support innovation while maintaining trust and compliance.

• Define and maintain data policies, standards and ownership models.

• Chair or support governance forums responsible for data quality, data usage and AI governance.

• Ensure governance processes remain practical, outcome-focused and business-led

Capability Building & Education

• Develop and deliver organisational education programmes on data literacy and AI literacy.

• Create practical training, champions network and awareness programmes to improve understanding

Data quality and Stewardship

• Responsible AI usage

• Structured and unstructured data

• Data-driven decision making

• Data governance responsibilities

• AI capabilities, tooling and opportunities

• Support business in creation of AI use cases

• Build a culture where data and AI is understood as a business asset rather than solely a technology concern.

• Provide coaching and support to senior leaders on data and AI opportunities and risks.

Business Partnership

• Act as the primary business-facing leader for data and AI value initiatives.

• Build strong relationships across business functions, technology teams and external stakeholders.

• Ensure data initiatives remain focused on practical business outcomes and measurable benefits.

• Work collaboratively across teams to remove barriers to adoption and value realisation.

Person Specification

Essential

• Experience leading data, business change, product or governance initiatives.

• Strong understanding of data management, data quality and governance principles in a pragmatic and small/medium environment working in an agile environment

• Knowledge of AI concepts and how data supports AI outcomes.

• Ability to translate complex data topics into practical business language.

• Strong stakeholder management and influencing skills.

• Experience establishing governance, assurance or control frameworks in data and AI

Desirable

• Experience within investment, membership, research, education or professional services environments.

• Experience developing data-enabled AI products or commercial services.

• Understanding of knowledge management and unstructured data practices.

• Experience designing and delivering learning programmes or capability-building initiatives.

• Familiarity with AI governance and emerging regulatory considerations.

We particularly welcome candidates from under-represented groups, including Black, Asian, and other People of Colour, those with visible or non-visible disabilities, LGBTQ+ candidates and those who are neurodivergent.

N.B. We reserve the right to close a vacancy before the closing date in the event of an overwhelming response or a change in business priorities.