Senior Director, US Decision Science & AI Innovation

Business Introduction
GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.
Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.

Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.

Position Summary
Are you a senior analytics leader who wants to set the scientific standard for how a major pharmaceutical business makes its commercial decisions — while staying hands-on in the methods themselves? If yes, this Senior Director, US Decision Science & AI Innovation role could be an ideal opportunity to explore.

This role owns the analytic and AI methods that turn data into commercial decisions across US Commercial — marketing mix and investment optimization, next-best-action and dynamic targeting, patient predictive triggering, and causal measurement — and their deployment into the business through enterprise capabilities. Success in this role requires strong partnership across GSK, including brand teams, Insights & Analytics, data engineering, the Digital & Technology organization, and data management.

The role sets what constitutes a defensible method, decides what is certified enterprise method versus brand configuration, and is accountable for the scientific quality, reproducibility and explainability of results used in brand and portfolio decisions. Accountability does not stop at the model: the role owns the decision engines, embedded field and omnichannel triggers, and planning and measurement tools through which methods reach users, and their adoption. A method or solution that is not used has not delivered.

As the organization moves to enterprise supply with brand-driven priorities, both the method and the capability carrying it must be built once, certified and reused rather than rebuilt brand by brand. This is a player-coach role, not an analytics oversight role — the post holder sets standards and develops the organization while working directly in the analytics, setting the technical bar by example. The role leads the applied AI agenda across agentic AI, generative AI and synthetic data, with a prototyping pipeline that graduates or retires techniques on evidence, and directs offshore GCC analytic capacity on methodology and development. Brand teams and Insights & Analytics retain ownership of interpretation and commercial action.

Key Responsibilities

• Own the certified method portfolio — marketing mix and investment optimization, next-best-action and dynamic targeting, patient predictive triggering, and causal measurement — driving reuse across brands and eliminating duplicate bespoke builds.

• Own deployment of those methods through enterprise capabilities — decision engines and simulators, embedded field and omnichannel triggers, planning and measurement tools — and be accountable for adoption and sustained use.

• Operate as a player-coach: personally design, review and build, setting the technical bar through direct involvement rather than oversight alone.

• Set and enforce method standards — validation, versioning, documentation, reproducibility and explainability — and define what is certified enterprise method versus brand configuration.

• Lead the applied AI agenda across agentic AI, generative AI and synthetic data — including multi-agent orchestration, retrieval-augmented generation and evaluation pipelines — with a prototyping pipeline whose explicit criteria either graduate techniques into production or retire them.

• Ensure methods, capabilities and AI applications meet GSK Responsible AI, privacy and data-use requirements, including model risk assessment, human oversight of automated recommendations, and permitted-use compliance for patient-level data, with a model registry and ongoing monitoring in place before anything scales.

• Partner with data engineering and the Digital & Technology organization for the platform and engineering these capabilities run on, and with data management…