Dir. Product Manager, Digital & Growth AI Tech

Business Introduction GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems. Our diverse portfolio consists of vaccines, specialty medicines, and general medicines. Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges. GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centered on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems. Our diverse portfolio consists of vaccines, specialty medicines, and general medicines. Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges. This role sits in the US Commercial and Digital Experience Technology team and owns the growth agenda: using generative and predictive AI to accelerate HCP and patient outcomes across the full commercial funnel — awareness, acquisition, conversion, adherence, and loyalty — and translating that into measurable brand growth (NRx/TRx lift, share, HCP reach and frequency, patient persistency). Every capability operates inside a heavily regulated pharma environment: FDA promotional rules, PhRMA Code, and Medical/Legal/Regulatory (MLR) review. You set the AI-for-growth vision, build the models-to-production pipeline in partnership with engineering and data science, and prove commercial impact brand by brand before scaling across the therapeutic portfolio. This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: Commercial Growth Agenda: Own the AI-driven growth capabilities roadmap across the HCP and patient funnel — awareness, acquisition, conversion, adherence/persistency, and loyalty — reporting impact in brand-relevant commercial terms (NRx/TRx, market share, customer (HCP, Patients) reach/frequency, patient starts rather than generic marketing metrics. AI Product Vision for Commercial Growth: Define the multi-year roadmap for applied AI across US Commercial and Digital experience — generative content engines, conversational AI, predictive customer (HCP and patient) models, next-best-action/next-best-offer — anchored to brand growth targets set with Commercial Leadership and Brand teams. Growth AI Agents: Build autonomous and semi-autonomous agents that generate, test, and optimize creative, targeting, and channel mix for customer (HCP and patient) support programs in real time, within approved claims and label language. Personalization at the Point of Prescribing/Care: Lead AI-driven personalization and dynamic content generation that shortens concept-to-MLR-approved-asset time, that plug into existing content and campaign management, CRM etc. platforms, reducing time from concept to MLR-approved asset. Model-to-Production for Commercial Use Cases: Partner with Data Science/ML Engineering to move growth models (LLMs, recommender systems, propensity/NBA models) from prototype to governed, monitored production services that meet GxP and GSK compliance expectations. Responsible AI & Pharma Regulatory Governance: Establish guardrails for model risk, HIPAA and global data-privacy equivalents, bias testing, and human-in-the-loop review; partner with Medical, Legal, Regulatory, and Compliance on an AI use-case intake and MLR-aligned approval process specific to promotional AI. AI Investment Strategy (Build-vs-Buy-vs-Partner): Decide which AI/foundation-model vendors and capabilities to pursue for growth outcomes, factoring in pharma-specific data-privacy and validation requirements. Ensure AI capabilities plug cleanly into the existing tech stack rather than creating parallel systems. Experimentation & Commercial Proof of Value: Run structured pilots — with Compliance sign-off — against brand-growth success metrics (Rx lift, conversion, patient starts, cost per acquisition) before greenlighting portfolio-wide rollout; kill or pivot underperforming bets quickly. Cross-Functional Influence in Commercial: Act as the primary translator between Brand/Commercial Leadership, Medical/Legal/Regulatory, Compliance, and Data Science on what AI can responsibly do today vs. next year for growth; brief Commercial executive stakeholders monthly on ROI and opportunities. Talent & Team Build: Hire and develop a small, senior team of AI-focused product managers and solutions architects embedded in Commercial; instill a test-and-learn culture grounded in real HCP, patient, and brand-performance feedback. Why You? Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals: Bachelor's degree in computer science, engineering, quantitative, or life-sciences 10&#43; years in product management with at least 2 years shipping AI/ML-powered or generative-AI features into production (not just pilots). Experience with LLM-based tooling — prompt/agent design, RAG architectures, or fine-tuning — applied to content generation, personalization and/ or omnichannel orchestration Experience in commercial growth mechanics: brand planning cycles, customer funnel metrics, market share, and how AI levers move each. Experience defining success metrics for AI initiatives in commercial terms, such as prescription volume, market share, and conversion impact, and using established governance and evidence-based criteria to continue, refine, or discontinue initiatives that do not meet agreed thresholds. Comfort partnering closely with data science/ML engineering on model evaluation, latency, cost, and monitoring trade-offs. Preferred Qualification If you have the following characteristics, it would be a plus Advanced degree in computer science, engineering, quantitative, or life-sciences field. <span style=