Data & AI Manager

Purpose

• The Data & AI Manager is responsible for defining and executing EIA’s data, analytics, and artificial intelligence agenda to
• enable data-driven decision-making across investment and corporate functions.
• Operating within a sovereign wealth fund context, the role focuses on building scalable data platforms, advanced
• analytics capabilities, and AI use cases that support investment performance, risk management, strategic planning, and
• operational efficiency, while ensuring strong governance, data quality, and responsible AI use. The role focuses on AI and
• analytics value creation, prioritisation, and adoption, working in partnership with IT for platform delivery and operations.

Strategic Responsibilities

• Define and lead EIA’s Data & AI Strategy, ensuring alignment with the Digital Strategy, investment priorities,

and corporate strategy.

• Establish enterprise-wide data governance frameworks, including data quality standards, ownership models,

and analytics principles.

• Identify, prioritize, and sequence AI and advanced analytics use cases that deliver measurable business value.
• Promote data literacy and adoption across investment and corporate teams to embed data-driven decisionmaking.
• Act as a strategic partner to senior leadership on data, analytics, and AI-enabled insights.

Core Responsibilities

1. Data Architecture & Analytics Platforms

• Define data and analytics requirements, standards, and target‑state architecture in collaboration with IT and

enterprise architecture.

• Ensure data and AI solutions meet business requirements for scalability, security, and governance, in

coordination with IT.

• Oversee data ingestion, transformation, and modelling to support analytics and reporting needs, with technical

data pipeline development and engineering owned by IT.

1. Analytics, AI & Decision Support

• Own the end‑to‑end AI use‑case lifecycle, including identification, prioritisation, business case development, value measurement, and adoption.
• Develop dashboards, predictive models, and decision-support tools for investment, strategy, risk, and performance teams.
• Lead the delivery of AI-enabled solutions, including advanced analytics, forecasting, and pattern detection.
• Ensure AI use cases are aligned with business objectives and supported by reliable data foundations.
• Technical build and platform operations are delivered jointly with IT and external partners.

1. Data Governance & Quality

• Ensure data quality, lineage, consistency, and transparency across enterprise systems.
• Implement data standards, controls, and monitoring mechanisms to support governance and audit requirements.
• Support responsible and ethical use of AI, including transparency, explainability, and appropriate human oversight.
• Develop AI acceptable‑use guidelines, human‑in‑the‑loop principles, and AI literacy programmes in partnership with Risk, Compliance, and HR.

1. Stakeholder Collaboration & Enablement

• Partner with IT and business stakeholders to implement analytics and AI solutions.
• Translate business needs into data and AI requirements and ensure solutions are adopted effectively.
• Support strategic initiatives through advanced analytics and insight generation.

People & Capability Responsibilities

• Build and develop data and analytics capabilities across teams.
• Establish knowledge-sharing practices, standards, and best practices for analytics and AI adoption.
• Support capability uplift through training, tools, and data literacy initiatives.

Key Deliverables

• Enterprise data platforms and standardized analytics dashboards.
• AI use cases supporting investment research, risk analysis, performance measurement, and strategic planning.
• Data governance frameworks, policies, and standards.
• Insight-driven support for strategic and investment initiatives.

Education & Qualifications

• Bachelor’s or master’s degree in data science, Computer Science, Engineering, Statistics, or a related field.

Experience Requirements

• 8–12 years of experience in data, analytics, or AI roles within complex organizations.
• Experience within sovereign wealth funds, asset owners or investment managers strongly preferred.
• Proven experience delivering enterprise analytics platforms and AI-driven solutions.
• Strong understanding of data platforms, cloud-based analytics, and machine learning concepts.

Technical Expertise / Skills / Knowledge

• Data architecture, analytics platforms, and reporting tools.
• AI and machine learning concepts and practical applications.
• Data governance, quality management, and metadata practices.
• Cloud-based data ecosystems and modern analytics stacks.
• Strong ability to translate complex data insights into business-relevant outcomes.
• Excellent stakeholder engagement and communication skills.