Principal (Senior Director), AI Product Management (Portfolio Systems)

GSK • Boston, MA

1d • $177,750 - $325,875

About The Position

GSK's Enterprise AI team translates AI into measurable impact across major business groups. This senior individual contributor will set the strategic direction for a portfolio of AI solutions supporting technology and R&D portfolio management, from intake, prioritization, and funding through resource allocation, benefits tracking, and portfolio reviews. The role identifies and ranks high-value opportunities, shapes the strategy that forward-deployed teams execute, and ensures engineering effort is directed toward the most impactful work. The position does not manage engineers; it determines what they build, where they focus, and how outcomes are measured.

The role combines strategic opportunity selection with hands-on prototyping, rigorous value assessment, and executive alignment. It turns ambiguous business needs into fundable engagements and practical plans for delivery and scale. It also designs the human-digital operating model for portfolio work, including digital-worker responsibilities and oversight, reusable agent skills, and trusted, continuous portfolio insight built from delivery, financial, usage, and AI-platform data. The role establishes Enterprise AI's value-realization approach, communicates impact to senior leaders, and applies learnings from deployments to future investment decisions.

Requirements

• At least 15 years of professional experience across some combination of pharma or 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 running or building technology or R&D portfolio-management systems, including at least one of intake, prioritization, funding stages, resource allocation, benefits tracking, or portfolio reviews.
• Hands-on experience with modern AI/ML concepts, including LLMs, agentic systems, digital workers, the ML lifecycle, and data and evaluation patterns, demonstrated through prototyping.
• Experience 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.
• Track record designing and shipping digital workers and agentic systems that perform operational work, including orchestration, tool use, evaluation, and maintaining reliability and observability in production.
• Experience designing shared libraries of agent skills, prompts, templates, and connectors for reuse across teams, including versioning, quality standards, ownership, and discovery.
• Direct involvement in operating-model design, defining portfolio decision rights, gates, cadences, roles, and workflows between people and digital workers.
• Working knowledge of instrumenting delivery and value data from engineering tools, financial systems, usage telemetry, and AI platforms, including the data modeling and governance needed for trusted continuous portfolio insight.

Nice To Haves

• Experience in forward-deployed engineering, applied AI, or an AI lab/center of excellence role at a pharma, healthcare, or comparable regulated enterprise.
• Background in management consulting, value engineering, or corporate strategy combined with hands-on technology experience.
• Experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments.
• Experience codifying product-management and user-research practices into AI-native tools, such as skills, plugins, and markdown files, and using the command line to automate daily work.

Responsibilities

• Identify, size, and prioritize AI opportunities across portfolio-management and value-realization workflows, maintaining a global portfolio ranked by impact, feasibility, and time-to-value.
• Build rigorous business cases that quantify outcomes such as time saved, faster decisions, avoided costs, and returned capacity.
• Develop expertise in portfolio-management workflows and identify AI opportunities that business teams may not have articulated.
• Maintain a living view of in-flight, queued, and de-prioritized Enterprise AI opportunities, including the rationale for decisions.
• Develop decision frameworks, define success metrics, analyze trade-offs, and recommend how engineering resources should be allocated when business units compete.
• Decline opportunities with evidence when they are unlikely to deliver scalable outcomes, protecting engineering focus.
• Partner with forward-deployed, full-stack, and data engineering leads to translate ambiguous business problems into problem statements, prototypes, success metrics, and decision frameworks.
• Shape engagements end to end by defining scope, success criteria, exit conditions, and scaling pathways, and remain engaged during execution.
• Identify opportunities to reuse solutions or create platform capabilities, working with engineering to make the case for reusable approaches.
• Design the division of work between digital workers, engineers, and business users, including evaluation and oversight to support safe operation.
• Redesign portfolio operating models for automation, defining decision rights, gates, cadences, roles, and workflows between people and digital workers.
• Build a reusable library of agent skills, prompts, templates, and connectors, with versioning, quality standards, ownership, and discoverability.
• Instrument real-time portfolio insight by integrating delivery and value data from engineering tools, financial systems, usage telemetry, and AI platforms, with appropriate data modeling and governance.
• Work with senior business and scientific leaders to turn broad ambitions into concrete, fundable AI engagements.
• Build sponsor coalitions and secure executive support, data access, and change-management commitment for engineering deployments.
• Communicate impact to executive leadership and the board, connecting AI outcomes to patients, science, and shareholder results.
• Establish the value-realization framework used by Enterprise AI to measure impact across deployments.
• Conduct post-mortems and feed lessons learned into opportunity selection and engagement design.
• Continuously refine the investment thesis for where AI can and cannot create value at GSK.

Benefits

• Annual bonus and eligibility to participate in a share-based long-term incentive program, depending on role level.
• Health care and other insurance benefits for employees and their families.
• Retirement benefits.
• Paid holidays and vacation.
• Paid caregiver, parental, and medical leave.

What This Job Offers

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