Principal (Senior Director), Commercial Enterprise AI Product Mgmt. - Knowledge Foundation and Insights Acceleration
Business Introduction
Responsibilities
Hunt for high-value AI opportunities
across the insight's acceleration lifecycle - from structuring PMR, SMR, competitive intelligence, and third-party market data (taxonomy, ontology, metadata, common data model, KPI framework) to exposing it through knowledge graphs and agentic AI interfaces - 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 - cycle time saved, decisions accelerated, revenue uplift, cost avoided, scientific productivity gained.
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Develop domain pattern recognition
Own the Enterprise AI opportunity portfolio,
Make hard trade-off calls —
Say no, with evidence,
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.
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Surface re-use and platform leverage,
Design the human-digital split,
Operate fluently with senior business and scientific leaders,
Build sponsor coalitions,
Communicate impact clearly,
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.
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Continuously sharpen the investment thesis
Basic Qualification
We are seeking professionals with the following required skills and qualifications to help us achieve our goals:
- 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.
- Hands-on work with the data foundations used in pharma market research and competitive intelligence, including taxonomy, ontology, metadata management, common data model design, or KPI frameworks, as applied to third-party data sets such as IQVIA or Komodo.
- Track record of building or specifying knowledge graphs and conversational or agentic AI interfaces that deliver insights directly to business decisionmakers.
- 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.
- History of operating as an individual contributor without direct reports and influencing engineering and business teams through frameworks and documented decision criteria.
- Prior responsibility for product management or product strategy, including opportunity sizing, roadmap development, prototyping, and stakeholder alignment.
- Work with the commercial analytics data landscape and the technology capabilities used to operationalize insights at scale.
- Direct involvement in analyzing intelligence sources and delivering insights used in business planning or portfolio decisions.
- Track record of building and maintaining knowledge bases spanning multiple commercial domains.
- Prior responsibility for aggregating regional market intelligence into national or global insights used for decision-making.
Preferred Qualification
If you have the following characteristics, it would be a plus
- Prior experience in a 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 with data foundation and knowledge platforms used in pharma insights, such as MDM/data fabric tools (Reltio, Informatica, Palantir Foundry), graph databases (Neo4j, Amazon Neptune), or vector search/RAG infrastructure.
#GSK-LI
Skills
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
- If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $195,525 to $325,875. The US salary ranges take into account a number of factors including work location within the US market, the candidate's skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process.
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
Uniting science, technology and talent to get ahead of disease together.
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Not included in the source posting: about the role, benefits.
Skills
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