Principal (Senior Director), Commercial Enterprise AI Product Mgmt. - E2E Marketing Enablement

Description

The company is a global biopharma business with a goal of £40 billion in annual sales by 2031. Its strategy centres on early intervention to prevent and alter the course of disease, across vaccines, specialty medicines and general medicines.

The Enterprise AI team turns AI into measurable results across R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance and other groups. This is a senior individual contributor role that sets the strategy for a portfolio of AI solutions across end-to-end marketing enablement, from modular scientific content to AI workflow orchestration through to how messages reach healthcare professionals. You will own the forward deployed strategy that each pod carries out.

You will not manage engineers. Instead you will decide what they build, where they deploy next, and how success is measured. You will find, size and rank the AI opportunities that matter most to the company, test proposed engagements for value, feasibility and strategic fit, and build the case, framing, prototype and executive support the pod needs to launch and scale.

Responsibilities

• Search for high-value AI opportunities across the marketing enablement lifecycle, including brief development, campaign strategy, global-to-local content adaptation, content supply chain, MLR review, channel activation and performance analytics. Build a global product portfolio ranked by impact, feasibility and time-to-value.
• Build business cases that finance teams and executive sponsors will trust, quantifying cycle time saved, faster decisions, revenue uplift, cost avoided and scientific productivity gained.
• Learn the patterns in marketing enablement workflows well enough to spot AI-suited problems the business has not yet put into words.
• Own the Enterprise AI opportunity portfolio, keeping a current view of in-flight, queued and deprioritised opportunities and the reason behind each decision.
• Make hard trade-off calls when business units compete for the same resources. Build the framework, define success metrics, run the analysis and recommend where engineers should go.
• Turn down attractive opportunities that will not produce scalable outcomes, backing the refusal with evidence and protecting engineering focus.
• Act as the strategic counterpart to the forward deployed, full-stack and data engineering leads. Turn unclear business problems into clear problem statements, prototypes, success metrics and decision frameworks that engineers can build against.
• Shape each engagement from start to finish, setting scope, success criteria, exit conditions and scaling paths before kick-off, and stay close to the work as engineers deliver it.
• Identify when a one-off solution should become a reusable capability and make that case with engineering.
• Decide which tasks the pod's digital workers own, where engineers and business users stay involved, and how evaluation and oversight keep that work safe.
• Lead executive conversations with senior business and scientific leaders that turn broad ambitions into concrete, fundable AI engagements.
• Build sponsor support that secures the backing, data access and change management engineering deployments need.
• Explain impact to the ELT and the board, linking AI work to patients, science and shareholder outcomes.
• Set up the value-realisation framework Enterprise AI uses to measure impact across deployments.
• Run candid post-mortems on what worked and what did not, and use the findings to improve opportunity selection and engagement design.
• Keep refining the investment thesis on where AI should and should not be used at the company.

Requirements

• 15 or more years of professional experience across pharma or life sciences, AI or ML product strategy, and product management or strategy in technology-led organisations.
• 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, covering at least one of: brief development, global-to-local content adaptation, content supply chain, MLR review, channel activation or performance measurement.
• Hands-on prototyping with modern AI and ML, including LLMs, agentic systems, digital workers, the ML lifecycle, and data and evaluation patterns.
• Experience working as an individual contributor with no direct reports, influencing engineering and business teams through frameworks and written decision criteria.
• Background in product management or product strategy, including opportunity sizing, roadmap development, prototyping and stakeholder alignment.
• Experience coordinating creative asset development and content submission workflows, including MLR readiness, so content can be deployed across channels.
• A record of turning omnichannel strategy into execution briefs for content and channel delivery teams.
• Direct experience working with internal MLR review teams and external agency or production partners on digital programmes.
• Working knowledge of content lifecycle traceability that links derived content back to its approved source.
• Nice to have: prior experience in forward deployed engineering, applied AI or an AI lab or centre of excellence role at a pharma, healthcare or comparable regulated enterprise.
• Nice to have: background in management consulting, value engineering or corporate strategy with hands-on technology experience.
• Nice to have: experience with GxP, regulatory and data-governance requirements in pharma AI deployments.
• Nice to have: 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.
• Skills listed: 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.

Conditions

For roles based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires ranges from $195,525 to $325,875. Pay depends on work location within the US market, skills, experience, education and the market rate for the role.

The position includes an annual bonus and eligibility for the share-based long-term incentive programme, depending on the level of the role.

Where a country's posting does not show a salary range, pay will be discussed during recruitment.

Equal-opportunity statement: the company welcomes applicants and makes accommodations available on request.

Benefits

• Health care and insurance for employees and their families, retirement benefits, paid holidays, vacation, and paid caregiver, parental and medical leave.
• Full details of the US benefits programme are available in the company's US benefits summary.