Staff (Director), Commercial Enterprise Product Mgmt. - E2E Marketing Enablement

GSK • Durham, NC

1d • $171,600 - $286,000 • Hybrid

About The Position

GSK’s Enterprise AI team applies AI across the company by embedding multidisciplinary bionic pods—including AI product managers, forward deployed engineers, full-stack engineers, data engineers, and digital workers—with scientific and business teams. This senior individual-contributor role owns a major part of the marketing enablement opportunity portfolio: marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement. The role sets the forward deployed strategy for its domain and partners with engineering and digital-worker teams to decide what to build and how to measure its impact; it does not manage engineers.

The AI Product Manager identifies, evaluates, and shapes valuable AI opportunities, develops defensible business cases, and aligns business sponsors and executives to fund and scale engagements. The role works with ambiguous business problems and probabilistic AI systems, using rapid prototyping, evaluation design, human oversight, and outcome measurement rather than conventional platform roadmaps and feature delivery. It also helps determine how engineers, business users, and digital workers divide work safely and effectively.

The role reports to the VP, Commercial AI/ML Transformation. It is hybrid, with a primary location at London New Oxford Street and secondary locations in Philadelphia, Boston, and Durham. Business travel between the UK, USA, and India is approximately 25%.

Requirements

• At least 10 years of progressive experience spanning some combination of pharma or life sciences, AI/ML product strategy, and senior product management or strategy roles in technology-led organizations.
• Demonstrated experience identifying, scoping, prototyping, and taking high-value technology or AI initiatives from problem framing through funding and delivery.
• Strong working knowledge of marketing enablement in pharma or another regulated industry, especially content supply chain, marketing automation, HCP-facing experiences, content assembly, campaign management, channel activation, and performance measurement.
• Technical fluency in modern AI/ML, including LLMs, agentic systems, digital workers, ML lifecycle, data patterns, and evaluation, sufficient to assess prototypes, challenge engineering proposals, and scope realistically.
• Experience as a senior individual contributor who leads through influence, frameworks, and quality of thinking rather than managing headcount.
• Product-management or product-strategy experience covering discovery, opportunity sizing, roadmap shaping, prototyping, and stakeholder alignment; AI-centered product experience is preferred.
• Experience building workflows for creative asset development and content submission, including MLR/MAC readiness, to support timely deployment across channels.
• Working knowledge of content lifecycle integrity and traceability, including maintaining links between derivative content and its approved source.
• Experience with omnichannel campaign execution and practices for measuring channel and content effectiveness.
• Ability to navigate GxP, regulatory, and data-governance constraints when designing AI engagements.
• Comfort with command-line interfaces and codifying product-management and user-research practices in skills, plugins, and markdown files.
• Strong analytical judgment, written communication, executive communication, and ability to turn ambiguity into actionable decisions.

Nice To Haves

• Experience managing products whose core behavior is AI rather than conventional software.
• Experience with pharma or life-sciences marketing enablement and regulated workflows.

Responsibilities

• Identify, assess, and structure high-value AI opportunities across marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement.
• Maintain an opportunity pipeline ranked by impact, feasibility, and time-to-value, and shape the domain portfolio with clear rationale for active, queued, and deprioritized work.
• Build rigorous, defensible business cases that quantify value such as time saved, faster decisions, revenue uplift, cost avoidance, and productivity gains.
• Recommend investment and engineering-resource trade-offs using evidence, and protect engineering focus from opportunities unlikely to produce scalable outcomes.
• Partner with forward deployed, full-stack, and data engineering leads to translate ambiguous business needs into problem statements, prototypes, success metrics, and decision frameworks.
• Define engagement scope, success criteria, exit conditions, and scaling pathways before kickoff, and stay engaged through execution.
• Identify opportunities to reuse solutions and transition one-off work into reusable capabilities or platforms.
• Design how engineers, digital workers, and business users divide tasks, hand work off, and provide escalation, evaluation, and oversight.
• Build alignment with VP-level business and scientific leaders and secure sponsor support for data access, change management, and deployment.
• Communicate AI impact in terms of patients, science, and enterprise outcomes.
• Apply and improve Enterprise AI’s value-realization framework, track engagement outcomes, and conduct post-mortems that inform future opportunity selection and delivery.
• Shape at least two flagship engagements per year and help set the forward deployed strategy for a bionic pod.

Benefits

• Annual bonus.
• Eligibility to participate in a share-based long-term incentive program, dependent on role level.
• Health care and other insurance benefits for employees and family.
• Retirement benefits.
• Paid holidays.
• Vacation.
• Paid caregiver, parental, and medical leave.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed