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

Description

GSK • Enterprise AI

About the Role

GSK’s Enterprise AI team, a highly skilled applied AI group, translates the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. Bionic pods — AI product managers, forward deployed engineers, full-stack engineers, data engineers, and digital workers — embed directly with scientific and business teams to build AI products that change outcomes.

We are hiring a Staff, Marketing Automation & AI Product Manager to own a major slice of our marketing enablement opportunity portfolio — marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement and act as the strategic counterpart to the engineers and digital workers in your pod, setting the forward deployed strategy they execute against. This is a senior individual contributor role for someone who can identify and shape high-value AI opportunities within marketing enablement, build the case to fund and deploy against them, and ensure FDEs are working on what matters most. You will not manage engineers; you will shape what they build in your domain and how we measure whether it worked.

Why This Role Exists

Forward deployed teams are most powerful when paired with sharp opportunity selection. Enterprise AI has no separate strategy layer: the AI Product Manager sets the forward deployed strategy, and the pod executes it. This role closes that gap at this level by ensuring the engineers we deploy are working on the right things.

In this role you will be the person who:

Owns a clear domain or capability area within the Enterprise AI opportunity portfolio.

Identifies, sizes, and shapes the highest-value AI opportunities in that domain.

Partners daily with forward deployed, full-stack, and data engineering leads to scope and steer engagements end-to-end.

Builds the business and executive alignment the pod needs to land and scale.

How This Differs from Traditional Product Management

This is not a traditional enterprise product role managing a source system’s roadmap, releases, and backlog. As an AI Product Manager in Enterprise AI, you:

Build new AI products from ambiguous, unsolved business problems rather than evolving an established platform.

Work with probabilistic systems — defining evaluations, acceptable error, and human oversight instead of fixed functional requirements.

Help lead a bionic pod, deciding which work goes to engineers and which to digital workers, and how they hand off.

Prototype hands on and are measured on business outcomes, not features shipped or system uptime.

Responsibilities

• Opportunity Identification & Value Sizing
• Hunt for high-value AI opportunities across marketing automation, content supply chain and assembly, the marketing experience layer, campaign management, and performance measurement; develop a structured pipeline of opportunities ranked by impact, feasibility, and time-to-value.
• Quantify value rigorously. Build credible business cases that survive finance and senior business unit scrutiny — cycle time saved, decisions accelerated, revenue uplift, cost avoided, scientific productivity gained.
• Develop deep domain pattern recognition so you can spot AI-shaped problems in marketing enablement workflows the business itself hasn’t yet articulated.
• Strategic Prioritization & Portfolio Shaping
• Shape the opportunity portfolio in your domain. Maintain a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision, and feed it into the broader Enterprise AI portfolio.
• Make trade-off recommendations. When two business sponsors both want an FDE pod, defines the success metrics, run the analysis and recommend where engineers go.
• Protect engineering focus. Push back — with evidence — on opportunities that won’t move scalable outcomes.
• Engineering Partnership & Enablement
• Be the strategic counterpart to forward deployed, full-stack, and data engineering leads in your domain. Translate ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
• Shape engagements end-to-end. Define scope, success criteria, exit conditions, and scaling pathways before kick-off, then stay close as engineers execute.
• Surface re-use and platform leverage when a one-off solution should become a reusable capability.
• Design the human–digital split. Decide 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.
• Stakeholder Alignment & Communication
• Operate fluently with VP-level business and scientific leaders. Run conversations that turn fuzzy ambitions into concrete, fundable AI engagements.
• Build sponsor coalitions that secure data access, change-management commitment, and air cover for your engagements.
• Communicate impact clearly in language that connects AI work to patients, science, and enterprise outcomes.
• Measurement, Learning & Iteration
• Apply Enterprise AI’s value-realization framework to every engagement in your domain and improve it with what you learn.
• Run honest post-mortems and feed lessons back into how your domain selects opportunities.
• Additional information:
• Reporting line: VP, Commercial AI/ML Transformation
• Position level: Director
• Team management: N/A
• Primary location: London New Oxford Street (UK)
• Secondary locations: Philadelphia (PA, US), Boston (MA, US), Durham (NC, US)
• Working model / presence onsite requirement: Hybrid
• Business travel: 25% travel between the UK, USA, and India
• Application end date: Sunday, November 8, 2026 EOD GMT
• What You Bring
• Required Functional Experience
• 10+ years of progressive experience spanning some combination of pharma/life sciences, AI/ML product strategy, and senior product management or strategy roles in technology-led organisations.
• Demonstrated track record of identifying, scoping, prototyping, and shepherding high-value technology or AI initiatives from problem framing through funding into delivery.
• Strong working knowledge of pharma or other regulated industry marketing enablement — particularly how content supply chain, marketing automation, and the HCP-facing experience layer operate end-to-end: content assembly, campaign management, channel activation, and performance measurement — and the data, decisions, and constraints inside them.
• Solid technical fluency in modern AI/ML — LLMs, agentic systems, and digital workers, ML lifecycle, data and evaluation patterns — sufficient to leverage evidence from rapid prototyping to challenge engineers, scope realistically, and avoid hype-driven choices.
• Experience operating as a senior individual contributor — leading through influence, frameworks, and quality of thinking rather than headcount.
• A product management or product-strategy heritage: discovery, opportunity sizing, roadmap shaping, prototyping, and stakeholder alignment — ideally on products whose core behaviour is AI rather than conventional software.
• Required Domain Experience
• Experience in building workflows for creative asset development and content submission workflows, including MLR/MAC readiness, to enable timely deployment across channels.
• Working knowledge of content lifecycle integrity and traceability — ensuring derivative content stays linked to its approved source throughout its lifecycle.
• Experience with omnichannel campaign execution and the performance measurement practices used to track channel and content effectiveness.
• How You Operate
• You think in terms of value, not activity.
• You convert ambiguity into clarity engineers and digital workers can act on.
• You bias toward small, rapid prototyping, sharp documents and decisions over slideware and process.
• You hold strong views, loosely held, and welcome being proven wrong by data.
• You take the long view on patient impact while caring deeply about shipping things this quarter.

Requirements

• The following skills define what strong performance looks like in this role across seven domains. Each descriptor reflects the scope, autonomy, and impact expected at this level.
• AI Opportunity Identification
• Opportunity Discovery: Leads opportunity discovery across a major domain (e.g. R&D, Medical, Commercial, Supply Chain, or an enterprise function). Builds and maintains a structured pipeline ranked by impact, feasibility, and time-to-value.
• Problem Framing: Frames problems at the domain level — defining the shape, scope, and strategic fit of AI opportunities before engineering investment is committed.
• Domain Pattern Recognition: Has developed deep domain pattern recognition in the assigned area. Spots AI-shaped problems in scientific and operational workflows the business itself has not yet articulated.
• Value Architecture & Business Case
• Value Sizing & Modeling: Builds value cases that survive finance and senior business unit scrutiny. Develops the team’s standard for what a credible value model looks like in each domain.
• Investment Prioritization: Makes trade-off recommendations at the domain level. When two sponsors both want an FDE pod, runs the analysis and recommends where engineers go — with evidence.
• Opportunity Portfolio Management: Shapes the domain-level portfolio. Maintains a living view with rationale for each decision. Feeds it into the broader Enterprise AI portfolio review.
• AI Product & Engagement Strategy
• Engagement Design & Scoping: Defines scope, success criteria, exit conditions, and scaling pathways before kick-off. Stays close through execution. Shapes at least two flagship engagements per year.
• Product Intuition & Roadmap Thinking: Brings a product management heritage to engagement strategy: discovery, opportunity sizing, roadmap shaping, and stakeholder alignment applied to AI deployments.
• Platform & Reusability Thinking: Identifies when one-off solutions should become reusable capabilities. Works with engineering to build the case and plan the transition from point solution to platform.
• Bionic Pod Orchestration: Designs how engineers and digital workers divide the work within the pod — task boundaries, hand-offs, escalation, and oversight.
• Technical Fluency & AI Literacy
• AI/ML Conceptual Fluency: Solid technical fluency sufficient to challenge engineers, scope realistically, and identify when a proposed approach is overcomplicated or underspecified.
• Data & Evaluation Judgment: Evaluates data and evaluation design as part of engagement scoping. Identifies when a proposed evaluation framework won’t hold up to scrutiny.
• AI Native : Codify product management and user researcher best practices skills, plugins, and markdown files. Comfortable with CLI to maximize daily automation activities.
• Regulated Environment AI Awareness: Navigates GxP, regulatory, and data-governance constraints fluently. Ensures FDE engagements are designed for the real compliance environment from the start.
• Stakeholder Influence & Executive Alignment
• Stakeholder Communication: Operates fluently with VP-level leaders. Runs conversations that turn fuzzy ambitions into concrete, fundable AI engagements. Builds sponsor coalitions that secure data access and change-management commitment.
• Influence Without Authority: Operates as a senior individual contributor — leads through frameworks, evidence, and credibility rather than headcount. Trusted by engineering and business alike.
• Executive Narrative & Impact Communication: Communicates AI impact clearly in language that connects to patients, science, and enterprise outcomes. Shapes the narrative for VP-level sponsorship and funding decisions.
• Measurement, Learning & Iteration
• Outcome Tracking: Applies the Enterprise AI value-realization framework to every engagement in the domain. Improves the framework with what each engagement teaches.
• Post-Mortem & Learning Culture: Runs post-mortems at the domain level. Feeds lessons back into opportunity selection, engagement design, and value modeling standards for the domain.
• Analytical Rigor: Sets the analytical standard for the domain. Challenges weak assumptions — including from senior business leaders — with evidence and alternative framing.
• Operating Model & Craft
• Artefact Quality & Written Clarity: Raises the team’s baseline artefact quality through example and direct feedback. Biases toward documents that accelerate decisions, not demonstrate effort.
• Autonomy & Ownership: Operates with significant autonomy across a defined domain. Makes domain-level decisions with confidence and escalates selectively, with context and a recommendation in hand.
• Intellectual Honesty & Evidence Orientation: Models intellectual honesty at the domain level. Protects engineering focus by saying no — with evidence — to opportunities that look attractive but won’t move outcomes.
• What Success Looks Like
• In the first 12 months, the person in this role will have:
• Built a healthy opportunity pipeline in your domain that the FDE leads actively use to plan deployments.
• Helped shape at least two flagship engagements with quantified, defensible value cases.
• Become the trusted go-to strategic partner for engineering and senior business leadership in your domain.
• Set the forward deployed strategy for at least one bionic pod, with digital workers carrying measurable production workload under clear human oversight.
• Improved hit-rate — a measurable shift in how often engagements in your domain deliver on the outcomes they set out to achieve.
• About GSK Enterprise AI
• Enterprise AI is GSK’s centre of gravity for applied artificial intelligence. We pair forward deployed, full-stack, and data engineers — working alongside digital workers in bionic pods — with scientific, clinical, and business teams across every major group at GSK to build AI products that get used — not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients.
• GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team — we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma.
• LI-GSK
• AGTech
• AI Platforms, Change Management, Data Analysis, Feature Prioritization, Machine Learning Model Management, ML Platforms, Model Evaluation, Performance Analysis, Product Delivery, Product Design, Product Lifecycle Management (PLM), Product Roadmapping, Roadmap Development, Statistical Analysis, User Needs Analysis
• If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $171,600 to $286,000. The US salary ranges take into account a number of factors including work location within the US market, the candidate’s skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process.
• Why GSK?
• Uniting science, technology and talent to get ahead of disease together.
• GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
• People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
• We believe in an agile working culture for all our roles. If flexibility is important to you, we encourage you to explore with our hiring team what the opportunities are.
• Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities contact us at UKRecruitment.Adjustments@gsk.com where you can also request a call.
• Please note should your enquiry not relate to adjustments, we will not be able to support you through these channels. However, we have created a Recruitment FAQ guide. Click the link where you will find answers to multiple questions we receive
• Important notice to Employment businesses/ Agencies
• GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.
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