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 how GSK runs its technology and R&D portfolios — from intake, prioritization, and funding stages through resource allocation, benefits tracking, and portfolio reviews — 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. Forward deployed teams are most powerful when paired with sharp opportunity selection: you will identify, size, and prioritize the AI opportunities that matter most to GSK, pressure-test proposed engagements against value, feasibility, and strategic fit, and build the case, framing, prototype, and executive alignment needed for the pod to land and scale — replacing stale, periodic portfolio reporting with continuous, trusted insight leaders are willing to act on.
Responsibilities
This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:
- Hunt for high-value AI opportunities across the portfolio management and value-realization lifecycle — intake, prioritization, funding stages, resource allocation, benefits tracking, and portfolio reviews — 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, cost avoided, and capacity returned to portfolio and delivery teams.
- Develop domain pattern recognition in portfolio management workflows — including what leadership actually needs from a portfolio view and why most portfolio reporting is stale before it is published — 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 gathering status, reconciling data across sources, flagging risks, and drafting analysis — where engineers and business users stay in the loop, and how evaluation and oversight make that safe.
- Redesign the portfolio operating model around automation, defining decision rights, gates, cadences, roles, and how work moves between people and digital workers, rather than bolting automation onto a manual process.
- Build a reusable skills library, establishing shared agent skills, prompts, templates, and connectors that multiple teams contribute to and reuse, with clear versioning, quality standards, ownership, and discovery.
- Instrument real-time portfolio insight, bringing together delivery and value data from engineering tools, financial systems, usage telemetry, and AI platforms, with the data modeling and governance that make leaders trust and act on it.
- Operate fluently with senior business and scientific leaders, running executive conversations that turn fuzzy ambitions 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 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.
- Direct involvement in running or building systems for technology or R&D portfolio management, including at least one of: intake, prioritization, funding stages, resource allocation, benefits tracking, or portfolio reviews.
- 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.
- Track record of designing and shipping digital workers and agentic systems that do real operational work — gathering status, reconciling data across sources, flagging risks, drafting analysis, and escalating to humans at the right moments — including orchestration, tool use, evaluation, and keeping agents reliable and observable in production.
- Prior responsibility for designing shared libraries of agent skills, prompts, templates, and connectors that multiple teams contribute to and reuse, including versioning, quality standards, ownership, and discovery.
- Direct involvement in operating model design, defining how a portfolio is run day to day — decision rights, gates, cadences, roles, and how work moves between people and digital workers.
- Working knowledge of instrumenting delivery and value data from engineering tools, financial systems, usage telemetry, and AI platforms, and the data modeling and governance needed to turn those signals into continuous, trusted portfolio insight.
Preferred Qualification
If you have the following characteristics, it would be a plus
- 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.
#GSK-LI #AGTech
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. • If you are based in another US location, the annual base salary range is $177,750 to $296,250. 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.
