Head of Data and Data Science, R&D, ED&AI

Overview

In this Director role, you’ll shape and drive data and data science strategy across R&D, aligning data capabilities with GSK’s Ahead Together mission. You will lead a multi-category data portfolio, govern data practices, and secure senior sponsorship for investments. Half of your time is hands-on, building and evaluating agentic AI systems and performing trusted data science work. You’ll partner with senior leaders to unlock high-value opportunities and scale data-driven decision making across R&D.

Responsibilities

• Define and execute a multi-year R&D data and data science roadmap across all data categories
• Lead and develop the TDPO organization with hiring, design, and career development
• Balance investment across engineering, data science, platform capacity, governance, and innovation
• Establish governance, review practices, and communities of practice to raise engineering and data science standards
• Drive data governance, privacy, metadata, access controls, and audit readiness across R&D data
• Set the data science and AI agenda with senior scientific leaders to identify high-value opportunities
• Create and scale agentic AI data science initiatives with appropriate human oversight
• Manage technology architecture, funding, partnerships, and supplier relationships
• Communicate portfolio health, risk, capacity, and value to senior R&D and Enterprise Data & AI leaders

Key requirements

• Experience leading a data, analytics, or data science organization in a regulated environment with budget and direct reports
• Experience guiding leaders who own significant scope (e.g., product owners, engineering managers)
• Track record of delivering a multi-year data and data science strategy with executive sponsorship
• Technical credibility in data engineering and data science for sound architecture and prioritization
• Willingness to spend ≥50% of time hands-on—coding, analysis, model development, agentic AI systems
• Experience building or deploying agentic AI systems for data-science tasks with oversight
• Knowledge of R&D data governance, including privacy, GxP, and audit readiness
• Executive communication and stakeholder management for portfolio-level decisions

• Strategic thinking
• Stakeholder management
• Influence without authority
• Data engineering
• Data science
• Agentic AI systems

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