Principal (Senior Director), AI Product Management (Legal & Compliance)

Position Summary

GSK's Enterprise AI team exists to translate the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. You will act as the strategic compass for driving a portfolio of AI solutions that transform Legal and Compliance across the globe — from contracting and litigation through investigations, privacy, and compliance monitoring — and own the forward deployed strategy each pod executes against.

This is a senior individual contributor role for someone who can see across GSK's value chain, identify where AI will create disproportionate value, prototype hands on, and ensure engineers are always deployed against the highest-impact opportunities. You will not manage engineers; you will shape what they build, where they go next, and how we measure whether it worked.

Responsibilities

• Hunt for high-value AI opportunities across Legal and Compliance — contracting, litigation and investigations, compliance monitoring, privacy, and the advisory work of the General Counsel's office — and develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value.
• Quantify value rigorously by building business cases that hold up with finance and executive sponsors — review hours returned, contract cycle time, outside counsel spend avoided, monitoring coverage, and risk surfaced earlier.
• Develop domain pattern recognition in legal and compliance workflows — including how matters flow between in-house counsel, legal operations, compliance officers, outside counsel, and the business — so you can spot AI-shaped problems the business itself hasn't yet articulated.
• Own the Enterprise AI opportunity portfolio, maintaining a living view of in-flight, queued, and de-prioritized opportunities, with the rationale for each decision.
• Make hard trade-off calls — building the framework, defining success metrics, running the analysis, and recommending where engineers go when business units compete for the same resources.
• Say no, with evidence, to opportunities that look attractive but won't move scalable outcomes, protecting engineering focus.
• Be the strategic counterpart to the forward deployed, full-stack, and data engineering leads, translating ambiguous business problems into crisp problem statements, prototypes, success metrics, and decision frameworks engineers can build against.
• Shape engagements end-to-end — defining scope, success criteria, exit conditions, and scaling pathways before kick-off, then staying close as engineers execute.
• Surface re-use and platform leverage, spotting when a one-off solution should become a reusable capability and working with engineering to make that case.
• Design the human-digital split, deciding which tasks the pod's digital workers own — such as contract review and drafting, clause extraction, first-pass document review, investigation triage, policy queries, and regulatory horizon scanning — where engineers and business users stay in the loop, and how evaluation and oversight make that safe.
• Design AI that lawyers can trust, building privilege preservation, litigation hold, defensible eDiscovery, anti-bribery and corruption obligations, data privacy and residency, and professional responsibility standards into every solution, with the accuracy and explainability legal work demands.
• Operate fluently with senior legal and compliance leaders, including the General Counsel, the Chief Compliance Officer, practice leads, and legal operations, running executive conversations that turn transformation priorities into concrete, fundable AI engagements.
• Build sponsor coalitions, securing the air cover, data access, and change-management commitment that engineering deployments require to succeed.
• Communicate impact clearly, telling the value story to ELT and the board, in language that connects AI work back to patients, science, and shareholder outcomes.
• Define how we keep score, establishing the value-realization framework Enterprise AI uses to measure impact across deployments.
• Run honest post-mortems — capturing what worked, what didn't, and feeding those learnings back into opportunity selection and engagement design.
• Continuously sharpen the investment thesis on where AI most belongs at GSK, and where it doesn't.

Basic Qualifications

• 15 or more years of professional experience across some combination of pharma/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, or close partnership with, legal and compliance work, including at least one of: contracting, litigation and investigations, compliance monitoring, privacy, or the advisory work of a General Counsel's office.
• Hands-on work with modern AI/ML concepts — including LLMs, agentic systems, and digital workers, the ML lifecycle, and data and evaluation patterns — demonstrated through prototyping.
• Prior responsibility for 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.
• Working knowledge of legal technology and the data behind it, such as contract lifecycle management (CLM), matter management, eDiscovery and investigation platforms, and AI tools built for legal work such as Harvey or Legora — including the realities of unstructured, confidential, and privileged data spread across systems with uneven metadata.
• Track record of identifying AI value in high-judgment, document-heavy work, such as contract review and drafting, clause extraction, first-pass document review, investigation triage, policy queries, regulatory horizon scanning, or compliance monitoring.
• Working knowledge of the professional, regulatory, and ethical constraints on legal AI, including legal privilege, litigation hold, eDiscovery defensibility, anti-bribery and corruption obligations, data privacy and data residency, and the professional responsibility standards that govern lawyers' use of AI.
• Direct involvement in partnering with senior legal and compliance leadership to turn transformation priorities into funded, measurable AI or technology programs.

Preferred Qualifications

• Prior experience in forward deployed engineering, applied AI, or AI lab/COE role at a pharma, healthcare, or comparable regulated enterprise.
• Background in management consulting, value engineering, or corporate strategy 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.