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About This Role
Position Summary
You will lead how GSK uses AI and digital products to deliver measurable outcomes in Patient Services - improving patient and provider experience, speeding access to therapy, and reducing the effort of patient support operations across CRM, portals, and apps. You will sit with Patient Services leaders, hub and field teams, and patients' and providers' journeys, understand how the work actually gets done, and decide where AI, agentic technology, and better digital design can make the biggest difference. Then you will own delivery end to end - discovery, build, evaluation, production deployment, and ongoing performance management.
This role sits at the intersection of product, engineering, and patient services operations. You will make your own feasibility and build-vs-buy calls, define what “good” looks like for non-deterministic AI systems, and move solutions from proof-of-concept into governed, monitored production use. Success is measured by adopted capabilities and quantified patient and operational impact.
We're looking for clear thinkers who combine patient services awareness with practical delivery skills - someone Patient Services leaders trust and technologist’s respect. This is a chance to shape where GSK invests in AI and digital across patient services, in support of our mission to unite science, technology, and talent to get ahead of disease together.
Key responsibilities include:
- Partner with Patient Services & Operations, Privacy, Compliance, Tech, and Data teams to turn pain points in patient journeys and hub operations into clear, specific problem statements.
- Identify where AI, agentic technology, and digital products can create the most value across CRM, portals, apps, enrollment, benefits investigation, case management, and patient and provider engagement - weighing impact, feasibility, and risk to prioritize investment.
- Build business cases with clear expected benefits, costs, and success measures; own the product strategy and roadmap, balancing quick wins with long-term capability, and assess build, buy, and partner options.
- Own delivery end to end - leading engineers, data scientists, UX/UI designers, and vendors through design, build, evaluation, and production deployment using Agile/Scrum, making key build and release decisions tied back to the business case.
- Partner with Data Engineering and data owners to secure the data foundation these products depend on - patient, case, consent, and CRM data quality, lineage, and master data alignment, plus API integrations and secure, standardized exchange of protected health information (PHI).
- Set the quality bar for products and AI solutions - validate through usability testing and evaluation before release, then own them after go-live: monitor product, model, prompt, and agent performance in production, manage versioning and change control, triage failure cases and drift, and run continuous improvement loops from user feedback and KPIs.
- Ensure responsible AI use and compliance, with human oversight, documentation, privacy, and governance built in from the start, aligned to GSK's security, quality, and AI governance standards and to HIPAA, GDPR, and other applicable regulations.
- Manage budgets, timelines, vendor performance, and delivery risk, flagging issues early.
- Prototype and validate solutions in the real world, then drive adoption — enablement, training, documentation, and SOP updates — and convert proven solutions into reusable patterns other Patient Services, Commercial, and D&T teams can adopt.
Why you?
Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- Bachelor's degree in business, computer science, data science, public health, or life sciences.
- 5+ years of experience in life sciences, specialty pharmacy, hub services, telehealth, digital health, creating and implementing digital solutions for patient services teams or healthcare providers.
- 3+ years of experience developing, implementing, or supporting digital solutions for patient services teams, healthcare providers, or patient engagement programs.
- Experience working with Salesforce Health Cloud, Salesforce Life Sciences Cloud, or a comparable CRM platform.
- Experience developing product roadmaps, business cases, project plans, or technology investment recommendations.Experience leading technology implementation projects through delivery and production deployment.
- Experience working in regulated healthcare, pharmaceutical, or life sciences environments.
- Experience supporting projects involving healthcare data privacy, security, or compliance requirements (e.g., HIPAA, GDPR).Experience with agile, scrum, or similar software delivery methodologies.
Preferred Qualifications:
If you have the following characteristics, it would be a plus
- Advanced degree (MBA, MSc, MPH, PhD) in a relevant discipline.
- Hands-on experience building and operating agentic AI or LLM-based workflows in production - not just pilots - from idea to measured outcome.
- Experience designing or operating agentic AI/automation platforms (e.g., LLM orchestration frameworks, workflow or agent builders) in a regulated enterprise setting.Familiarity with cloud AI platforms (Azure AI, Google Cloud, AWS) and MLOps/LLMOps concepts.
- Experience with human-in-the-loop systems, prompt engineering, or deploying generative AI in regulated environments.
- Experience with health information exchange standards and assessing AI vendors or platforms.
- Experience partnering with UX/UI designers to prioritize user needs and deliver intuitive interfaces.
- Experience managing external vendors, consulting partners, or delivery teams on technology programs.
- Experience leading transformation, merger integration, or operating model change.
Skills
Agile Methodology, Cross-Functional Teamwork, Data Analysis, Design Thinking, DevOps, Prioritization, Product Development Research, Product Roadmapping, Project Management, Return on Investment (ROI), Software Development, Strategy Deployment, User Experience (UX), User Interface (UI) Design
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
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Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 11,520 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At GSK, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 993 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
GSK AI Hiring
GSK has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span London, England, UK, Philadelphia, PA, Collegeville, PA, US. Compensation range: $226K - $226K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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