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GSK

Develops medicines, vaccines, and consumer health

Enterprise AI Product Strategy Principal – Senior Director - Commercial Enterprise AI Product Management, End-to-End Marketing Enablement

Product ManagementConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • At least 15 years of professional experience across some combination of pharma or life sciences, AI/ML product strategy, and product management or strategy roles in technology-led organizations.
  • A track record of taking at least one technology or AI initiative from problem framing through executive funding and into delivery.
  • Direct involvement in pharma marketing enablement, including at least one of brief development, global-to-local content adaptation, content supply chain, MLR review, channel activation, or performance measurement.
  • Hands-on work prototyping with modern AI/ML concepts, including large language models, agentic systems, digital workers, the machine learning lifecycle, and data and evaluation patterns.
  • Prior responsibility for working as an individual contributor without direct reports and influencing engineering and business teams through frameworks and documented decision criteria.
  • A product management or product strategy background, including opportunity sizing, roadmap development, prototyping, and stakeholder alignment.
  • Prior responsibility for coordinating creative asset development and content submission workflows, including MLR readiness, to enable deployment across channels.
  • A track record of translating omnichannel strategy into execution briefs for content and channel delivery teams.
  • Direct involvement in partnering with internal MLR review processes and external agency or production partners on digital program delivery.
  • Working knowledge of content lifecycle traceability processes linking derivative content to its approved source.

Responsibilities

  • Identify and prioritize high-value AI opportunities across the end-to-end marketing enablement lifecycle, including brief development and campaign strategy, global-to-local content adaptation, content supply chain and MLR review, channel activation, and performance analytics; develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.
  • Quantify value by building business cases for finance and executive sponsors, covering cycle time saved, decisions accelerated, revenue uplift, cost avoided, and scientific productivity gained.
  • Develop domain pattern recognition in end-to-end marketing enablement workflows to identify AI-shaped problems the business has not yet articulated.
  • Own the Enterprise AI opportunity portfolio, maintaining a current view of in-flight, queued, and de-prioritized opportunities and the rationale for each decision.
  • Build a framework, define success metrics, run analysis, and recommend where engineers should be deployed when business units compete for resources.
  • Decline opportunities that will not produce scalable outcomes, using evidence to protect engineering focus.
  • Partner strategically with forward-deployed, full-stack, and data engineering leads, translating ambiguous business problems into problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
  • Shape engagements end to end by defining scope, success criteria, exit conditions, and scaling pathways before kickoff, then staying close as engineers execute.
  • Identify opportunities for reuse and platform leverage, and work with engineering to make the case for turning one-off solutions into reusable capabilities.
  • Design the human-digital split by deciding which tasks digital workers own, where engineers and business users remain involved, and how evaluation and oversight ensure safety.
  • Run executive conversations with senior business and scientific leaders to turn ambiguous ambitions into concrete, fundable AI engagements.
  • Build sponsor coalitions to secure executive support, data access, and change-management commitment for engineering deployments.
  • Communicate impact to executive leadership and the board, connecting AI work to patients, science, and shareholder outcomes.
  • Establish the value-realization framework Enterprise AI uses to measure impact across deployments.
  • Conduct post-mortems, capture what worked and what did not, and feed the learnings back into opportunity selection and engagement design.
  • Continuously refine the investment thesis on where AI belongs at GSK and where it does not.

Desired Qualifications

  • Prior experience in forward-deployed engineering, applied AI, or an AI lab or center of excellence role at a pharma, healthcare, or comparable regulated enterprise.
  • Background in management consulting, value engineering, or corporate strategy with hands-on technology experience.
  • Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
  • Experience with marketing technology platforms used in content and channel operations, such as Veeva Vault PromoMats or MedComms, Adobe Experience Manager or Workfront, Salesforce Marketing Cloud, MLR review platforms, or omnichannel engagement and analytics tools.

About the Company

GSK

Develops medicines, vaccines, and consumer health

GSK is a global healthcare company focused on three main areas: Pharmaceuticals, Vaccines, and Consumer Healthcare. It develops medicines, vaccines, and consumer health products to improve health outcomes worldwide. Its products address diseases in respiratory, HIV, oncology, and immuno-inflammatory areas; vaccines for influenza, shingles, and COVID-19; and over-the-counter wellness products. The company relies on substantial R&D and strategic partnerships to bring new products to market and to address health needs. Revenues come from sales of medicines, vaccines, and consumer health items, often complemented by patient support programs and collaborations with governments and biotech partners.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1891

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