Description
GSK's Enterprise AI team exists to translate the promise of AI into measurable impact across every major group at GSK. You will act as the strategic compass for driving a portfolio of AI solutions that advance end-to-end marketing enablement across the globe.
Responsibilities:
- Hunt for high-value AI opportunities across the end-to-end marketing enablement lifecycle and develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.
- Quantify value rigorously by building business cases that hold up with finance and executive sponsors.
- Develop domain pattern recognition in end-to-end marketing enablement workflows so you can spot AI-shaped problems the business itself hasn't yet articulated.
- Own the Enterprise AI opportunity portfolio, maintaining a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision.
- Make hard trade-off calls , building the framework, defining success metrics, running the analysis, and recommending where engineers go when business units compete for the same resources.
- Say no, with evidence, to opportunities that look attractive but won't move scalable outcomes, protecting engineering focus.
- Be the strategic counterpart to the forward deployed, full-stack, and data engineering leads, translating ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
- Shape engagements end-to-end , defining scope, success criteria, exit conditions, and scaling pathways before kick-off, then staying close as engineers execute.
- Surface re-use and platform leverage, spotting when a one-off solution should become a reusable capability and working with engineering to make that case.
- Design the human-digital split, deciding which tasks the pod's digital workers own, where engineers and business users stay in the loop, and how evaluation and oversight make that safe.
- Operate fluently with senior business and scientific leaders, running executive conversations that turn fuzzy ambitions into concrete, fundable AI engagements.
- Build sponsor coalitions, securing the air cover, data access, and change-management commitment that engineering deployments require to succeed.
- Communicate impact clearly, telling the value story to ELT and the board, in language that connects AI work back to patients, science, and shareholder outcomes.
- Define how we keep score, establishing the value-realization framework Enterprise AI uses to measure impact across deployments.
- Run honest post-mortems , capturing what worked, what didn't, and feeding those learnings back into opportunity selection and engagement design.
- Continuously sharpen the investment thesis on where AI most belongs at GSK, and where it doesn't.
Basic Qualifications:
- 15 or more years of professional experience across some combination of pharma/life sciences, AI/ML product strategy, and product management or strategy roles in technology-led organizations.
- 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 with modern AI/ML concepts , including LLMs, agentic systems, and digital workers, the ML lifecycle, and data and evaluation patterns , demonstrated through prototyping.
- Prior responsibility for operating as an individual contributor without direct reports and influencing engineering and business teams through frameworks and documented decision criteria.
- Background in product management or product strategy, 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.
- 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 that link derivative content to its approved source.
Preferred Qualifications:
- Prior experience in forward deployed engineering, applied AI, or AI lab/COE 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/MedComms, Adobe Experience Manager or Workfront, Salesforce Marketing Cloud, MLR review platforms, or omnichannel engagement/analytics tools.