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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in AI/ML product strategy and portfolio management within the pharma/life sciences sector, focusing on opportunity identification, business case development, and operational model design. Proven ability to influence cross-functional teams and drive AI initiatives from conception to delivery while ensuring compliance with regulatory standards.
Highest-signal resume keywords
AI/ML Product StrategyPortfolio ManagementBusiness Case DevelopmentOperational Model DesignRegulatory Compliance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Opportunity IdentificationBusiness Case QuantificationDecision Framework DevelopmentDigital Worker DesignAgentic Systems PrototypingPortfolio Review ManagementData ModelingValue TrackingResource AllocationStakeholder Alignment
Soft Skills
Strategic PartnershipInfluencing Without AuthorityExecutive CommunicationProblem FramingCollaboration
Tools & Technologies
AI PlatformsEngineering ToolsFinancial SystemsUsage TelemetryVersion Control Systems
Industry Keywords
PharmaLife SciencesGxPRegulatory RequirementsData Governance
About the role
Key responsibilities & impact- Hunt for high-value AI opportunities across the portfolio management and value-realization lifecycle
- Develop a global portfolio of AI products ranked by impact, feasibility, and time-to-value
- Build rigorous business cases quantifying cycle time saved, decisions accelerated, cost avoided, and capacity returned
- Develop domain pattern recognition in portfolio management workflows and identify AI-shaped problems
- Own the Enterprise AI opportunity portfolio, including in-flight, queued, and de-prioritized opportunities and decision rationales
- Build trade-off frameworks, define success metrics, run analysis, and recommend engineering resource allocation
- Evaluate and reject opportunities that will not produce scalable outcomes
- Partner strategically with forward deployed, full-stack, and data engineering leads
- Translate ambiguous business problems into problem statements, prototypes, success metrics, and decision frameworks
- Shape engagements end-to-end, including scope, success criteria, exit conditions, and scaling pathways
- Identify re-use and platform leverage and help develop reusable capabilities
- Design the human-digital split, including digital-worker responsibilities, human oversight, evaluation, and safety
- Redesign the portfolio operating model around automation, decision rights, gates, cadences, roles, and workflows
- Build a reusable library of agent skills, prompts, templates, and connectors with versioning and quality standards
- Instrument real-time portfolio insight using delivery and value data from engineering tools, financial systems, usage telemetry, and AI platforms
- Run executive conversations and build sponsor coalitions for AI engagements
- Communicate impact to ELT and the board in terms connected to patients, science, and shareholder outcomes
- Define Enterprise AI’s value-realization framework and measurement approach
- Run post-mortems and feed learnings into opportunity selection and engagement design
- Continuously refine the investment thesis for where AI should and should not be applied at GSK
Requirements
What you’ll need- 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 intake, prioritization, funding stages, resource allocation, benefits tracking, or portfolio reviews
- Hands-on work with modern AI/ML concepts, including LLMs, agentic systems, 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 perform operational work, including orchestration, tool use, evaluation, and production reliability and observability
- Prior responsibility for designing shared libraries of agent skills, prompts, templates, and connectors, including versioning, quality standards, ownership, and discovery
- Direct involvement in operating model design, defining 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 data modeling and governance
- Preferred: experience in forward deployed engineering, applied AI, or AI lab/COE roles at a pharma, healthcare, or comparable regulated enterprise
- Preferred: background in management consulting, value engineering, or corporate strategy with hands-on technology experience
- Preferred: experience navigating GxP, regulatory, and data-governance requirements in pharma AI deployments
- Preferred: experience codifying product management and user research practices into AI-native tools and using the command line to automate daily work
Benefits
Comp & perks- Annual bonus
- Eligibility to participate in a share based long term incentive program
- Health care and other insurance benefits for employee and family
- Retirement benefits
- Paid holidays
- Vacation
- Paid caregiver/parental and medical leave
- Accommodation or other assistance to apply, if required