Staff, AI Product Manager (Development Engine)

GSK•Upper Providence, PA

1d•Hybrid

About The Position

GSK’s Enterprise AI team turns AI potential into measurable impact across drug discovery, development, manufacturing, and commercial operations. Forward deployed teams work closely with scientific and business teams to develop AI products. This role is in Chemical Manufacturing & Control (CMC), which turns validated molecules into reliably manufactured, quality-assured medicines, and has a mission to double New Product Introductions (NPIs). The Staff AI Product Manager owns a major part of the Enterprise AI opportunity portfolio for process development. In partnership with business stakeholders, the role identifies and prioritizes opportunities according to feasibility and value, structures problems, sizes potential value, prototypes solutions, and defines work for forward deployed engineers. The role focuses on transformational process-development opportunities, including self-driving labs, digital twins, in silico modeling, platform models, and AI-enabled R&D workflows such as document authoring. It partners closely with process science, data analytics, and R&D technology teams to deliver measurable impact. As a senior individual contributor, the position helps ensure engineering teams focus on the highest-value work. It owns an opportunity domain or capability area, works closely with forward deployed and full-stack engineering leads to shape engagements from discovery through scaling, and builds the business and executive alignment needed to fund and expand AI initiatives. The role is hybrid in the United States, with scheduled on-site collaboration; fully remote work is not available.

Requirements

Bachelor’s degree or equivalent experience in technology, computer science, product management, or a related field.
At least 10 years of experience across pharmaceutical or life sciences, AI/ML product strategy, or senior product management or strategy roles in technology-led organizations.
Experience leading technology or AI initiatives from problem framing through funding and into delivery.
Experience in at least one pharmaceutical value-chain area: R&D, clinical, manufacturing, or commercial.
Experience applying AI/ML technologies such as large language models, agentic systems, knowledge graphs, or data engineering.
Experience building business cases and sizing value for R&D initiatives.
Experience in product management or product strategy, including opportunity sizing, roadmap development, and prototyping.
Experience with modeling, digital twins, self-driving labs, or physical AI in pharmaceutical R&D or process development, including defining pilots and evaluating results with scientists.
Experience defining a vision and roadmap for self-driving labs and lab automation in partnership with scientists and automation teams.
Experience partnering with data analytics, data science, or process science teams on modeling, optimization, and simulation.
Experience as a senior individual contributor who leads through influence rather than direct reports.

Nice To Haves

Master’s or PhD in technology, computer science, product management, or a related field.
Ability to use evidence from rapid prototyping to challenge engineering choices and scope work realistically.
Experience leading AI opportunity discovery and problem framing across a major domain such as R&D, clinical, or supply chain, including building a pipeline ranked by impact, feasibility, and time-to-value.
Experience co-leading self-driving lab or lab automation initiatives, including automating experiment planning, execution, and data capture and linking labs to in silico models.
Experience building value cases and investment recommendations that withstand finance and senior leadership scrutiny, and managing an opportunity portfolio.
Experience scoping AI engagements end to end, including success criteria, exit conditions, and scaling pathways, and turning one-off solutions into reusable platform capabilities.
Technical fluency in AI/ML, data, and evaluation design, with hands-on use of AI tools and command-line interfaces to automate daily work.
Experience working within good practice (GxP), regulatory, and data-governance requirements in a regulated environment.
Experience influencing VP-level leaders without direct authority, communicating AI impact in terms of patients, science, and business outcomes, and using outcome tracking and post-mortems to improve future work.

Responsibilities

Identify and rank high-value AI opportunities within the assigned domain by impact, feasibility, and time-to-value.
Build rigorous business cases and quantify value for R&D initiatives in a way that meets finance and senior leadership scrutiny.
Identify AI opportunities in workflows, including problems not yet articulated by business stakeholders.
Co-lead the redesign of self-driving labs and define the 2033 vision and roadmap for lab automation with forward deployed engineering pods and automation teams.
Co-lead the digital twin agenda and advance in silico modeling with an end-to-end platform mindset.
Define platform fitness for molecule onboarding and track progress toward halving development time.
Maintain a current portfolio view of in-flight, queued, and de-prioritized opportunities.
Recommend trade-offs when sponsors compete for engineering capacity, using clear metrics and analysis.
Protect engineering focus by challenging low-impact opportunities with evidence.
Partner with forward deployed and full-stack engineering leads to turn ambiguous problems into clear problem statements, prototypes, and success metrics.
Shape engagements end to end, including scope, success criteria, exit conditions, and paths to scale.
Promote reuse by converting one-off solutions into platform capabilities.
Engage VP-level leaders to develop concrete, fundable AI engagements.
Build sponsor coalitions that secure data access, commitment to change, and executive support.
Communicate impact in terms of patients, science, and enterprise outcomes.
Apply the value-realization framework to engagements and refine it based on experience.
Conduct post-mortems and use lessons learned to improve opportunity selection.