Manager II, AI Product

GSK’s Enterprise AI team translates the promise of AI into measurable impact across drug discovery, development, manufacturing, and commercial. Forward deployed engineers (FDEs) and full-stack engineers embed directly with scientific and business teams to build AI products that change outcomes.

We are hiring a Product Manager II, AI Product Manager to support our forward deployed teams working with GSK’s manufacturing sites. This is an individual contributor role for someone early- to mid-career who is hungry to do the analytical, hands-on, and stakeholder-facing work that makes AI engagements succeed: opportunity discovery, value sizing, scoping, hands-on prototyping, delivery, and ongoing measurement. You will be supported by senior team members, but the work — the briefs, the analyses, the value cases, the scoping docs, and getting things over the line — will be yours to drive.

This role is execution first. You will work directly with site teams in production, planning, quality, and training, and iterate with them on the data, the model outputs, and the workflow until the solution works on the floor. You are comfortable rolling up your sleeves: pulling and checking data, testing outputs, running user sessions, and closing the loop between the site and the engineers.

Forward deployed teams are most powerful when paired with sharp opportunity selection and disciplined execution. This role closes that gap at this level by ensuring the engineers we deploy are working on the right things, and that what they build lands with the people at the site who will use it.

In this role you will be the person who:

• Drives opportunity discovery and value-case work on specific manufacturing engagements, supported by senior team members.

• Acts as a working partner to FDE pods on day-to-day scoping, delivery, and measurement.

• Works hands-on with site stakeholders, iterating on data, model outputs, and workflows until solutions are fit for use.

• Builds the analytical artefacts (briefs, value models, scoping docs, early prototypes) the team relies on.

Opportunity Identification & Value Sizing

• Run opportunity discovery with site and business stakeholders — interviews, workflow walk-throughs, time on the production floor, light data analysis — to surface AI-shaped problems worth pursuing.

• Build first-cut value cases with clear assumptions, rough sizing, and a credible path to validation, using manufacturing metrics such as throughput, schedule adherence, right-first-time, labour productivity, and training cycle time.

• Document opportunities in a consistent, comparable format so they can be ranked and prioritized.

Strategic Prioritization & Portfolio Shaping

• Help senior team members prioritize by gathering the evidence, building the comparison views, and pressure-testing assumptions.

• Track the opportunity backlog and keep it tidy, current, and decision-ready.

Execution & Hands-On Delivery

• Own the execution of your assigned workstreams: plans, actions, dependencies, and follow-through, so engagements keep moving from prototype to production.

• Get hands-on when needed: pull, explore, and sanity-check site data; build quick analyses or low-code prototypes; test model outputs against what the site expects.

• Iterate with site stakeholders on data quality, model outputs, and user feedback. Run review and user-testing sessions, capture what is wrong or missing, and turn it into clear asks for the engineers.

• Support site rollout and adoption: user guides, training sessions, feedback loops, and tracking whether the solution is actually being used.

Engineering Partnership & Enablement

• Be a working partner to FDE pods. Sit close to the work, capture decisions, surface blockers, and turn them into clear asks for stakeholders.

• Maintain engagement scoping documents and keep them current as scope evolves.

• Take great notes that synthesize into great artefacts — scoping briefs, decision logs, value memos.

Stakeholder Alignment & Communication

• Run productive working-level meetings with site and business stakeholders, from operators and supervisors to site leads.

• Communicate clearly in writing — short, structured updates that respect stakeholders’ time.

Measurement, Learning & Iteration

• Track engagement outcomes against the value case and flag drift early.

• Capture lessons learned in a consistent format the team can search and reuse.

Required Experience

• 5+ years of experience in some combination of manufacturing, operations, strategy/consulting, product management, AI/ML product, or analytical roles in pharma, healthcare, or other regulated or technology-led organisations.

• An understanding of the manufacturing business — how production, planning, workforce, quality, and training work at a site — ideally gained through working at or closely with a manufacturing site.

• A track record of execution: getting projects delivered, not just planned, in a fast-moving, hands-on environment.

• Willingness and ability to get hands-on with data and tools (for example Excel, SQL, Python, or low-code tools) to explore data, check model outputs, and prototype quickly.

• Strong analytical and structured-thinking skills — you can take a fuzzy ask and produce a tight, well-reasoned brief.

• Comfortable technical fluency in modern AI/ML — enough to participate credibly in engineering scoping conversations and to explain model outputs to site users.

• Excellent written and verbal communication; you produce artefacts senior people will read and act on, and you can work just as well with operators on the floor.

• Automate daily tasks using skills, plugins, and markdown.

• High agency: you take work to a finished state without needing to be chased.

How You Operate

• You think in terms of value, not activity.

• You are execution focused: you get things done and close the loop.

• You roll up your sleeves when needed, and you go to the site to see the work for yourself.

• You convert ambiguity into clarity engineers can act on.

• You bias toward small, sharp documents and decisions over slideware and process.

• You hold strong views, loosely held, and welcome being proven wrong by data.

• You take the long view on patient impact while caring deeply about shipping things this quarter.

The following skills define what strong performance looks like in this role across seven domains. Each descriptor reflects the scope, autonomy, and impact expected at this level.

AI Opportunity Identification

• Opportunity Discovery: Conducts structured stakeholder interviews, workflow walkthroughs, and site visits to surface AI-shaped problems. Documents findings consistently in comparable formats.

• Problem Framing: Translates fuzzy stakeholder asks into structured problem statements with clear boundaries. Produces tight, readable opportunity briefs.

• Domain Pattern Recognition: Working understanding of manufacturing site operations. Can recognise obvious AI-shaped workflows in production, planning, workforce, and training with guidance.

Value Architecture & Business Case

• Value Sizing & Modeling: Builds first-cut value cases with stated assumptions, rough sizing, and a credible path to validation. Flags where assumptions are weakest.

• Investment Prioritization: Gathers evidence and builds comparison views to help senior team members prioritize opportunities. Pressure-tests assumptions under guidance.

• Opportunity Portfolio Management: Tracks the opportunity backlog for assigned engagements. Keeps it current, tidy, and decision-ready. Flags drift from agreed scope early.

AI Product & Engagement Strategy

• Execution & Delivery: Owns the execution of assigned workstreams end-to-end. Plans, follows through, and removes small blockers so work moves from prototype to production at the site.

• Engagement Design & Scoping: Maintains engagement scoping documents and keeps them current as scope evolves. Captures decisions and turns them into clean artefacts (decision logs, value memos).

• Product Intuition & Roadmap Thinking: Applies product management basics — user needs, success metrics, iteration — to AI engagement work. Learning to think in product arcs, not one-off deliveries.

• Platform & Reusability Thinking: Flags potential reuse patterns across sites to senior team members when spotted in the work. Learning to distinguish point solutions from platform opportunities.

Technical Fluency & AI Literacy

• AI/ML Conceptual Fluency: Comfortable technical fluency in modern AI/ML — enough to participate credibly in engineering scoping conversations. Understands what LLMs, agentic systems, optimisation models, and ML pipelines do without needing to build them.

• Data & Evaluation Judgment: Gets hands-on with site data to check quality, access, and completeness. Reviews model outputs with site users, spots where they are wrong or unhelpful, and feeds that back to engineering.

• AI Native: Codify product management and user researcher best practices skills, plugins, and markdown files. Comfortable with CLI to maximize daily automation activities.

• Regulated Environment AI Awareness: Developing awareness of GxP, regulatory, and data governance constraints relevant to AI at manufacturing sites. Knows when to escalate a compliance question.

Stakeholder Influence & Executive Alignment

• Stakeholder Communication: Runs productive working-level meetings. Communicates clearly in writing — short, structured updates that respect stakeholders’ time and produce decisions, not just notes.

• Site Stakeholder Iteration: Works side by side with site users and owners, iterating on data, model outputs, and workflow fit until the solution is trusted and used.

• Influence Without Authority: Builds working relationships with engineering partners and site stakeholders. Earns trust through the quality of artefacts, responsiveness, and follow-through, not title.

• Executive Narrative & Impact Communication: Produces well-structured written updates. Learning to connect AI work to business outcomes in language non-technical stakeholders understand.

Measurement, Learning & Iteration

• Outcome Tracking: Tracks engagement outcomes, including adoption at the site, against the value case. Flags drift from plan early and captures lessons learned in a consistent, reusable format.

• Post-Mortem & Learning Culture: Captures lessons learned after each engagement in a consistent format. Makes the output findable and reusable by the broader team.

• Analytical Rigor: Thinks with structured, logical reasoning. Can take a fuzzy ask and produce a tight, well-reasoned brief with clear assumptions and stated uncertainties.

Operating Model & Craft

• Artefact Quality & Written Clarity: Produces artefacts (briefs, value models, scoping docs, decision logs) that senior people will read and act on. Takes work to a finished state without needing to be chased.

• Autonomy & Ownership: High agency: takes work to a finished state without being chased. Proactively surfaces blockers and turns them into clear asks for stakeholders or senior team members.

• Intellectual Honesty & Evidence Orientation: Holds strong views, loosely held. Welcomes being proven wrong by data or a better argument. Does not anchor to initial estimates when evidence contradicts them.

In the first 12 months, the person in this role will have:

• Produced a steady stream of crisp, decision-ready opportunity briefs and value cases.

• Helped take at least one AI solution from prototype into live use at a manufacturing site, iterating with site users on data and model outputs along the way.

• Become a trusted working partner to FDE pods and site teams — the person engineers and site stakeholders are glad to have in the room.

• Built durable artefacts and templates that lift the team’s baseline quality.

• Earned the autonomy to lead engagements end-to-end at the Senior AI Product Manager (G6) level.

Enterprise AI is GSK’s centre of gravity for applied artificial intelligence. We pair forward deployed engineers and full-stack engineers with scientific, clinical, and business teams to build AI products that get used — not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients.

GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team — we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma.

Skills

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.

GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.

We believe in an agile working culture for all our roles. If flexibility is important to you, we encourage you to explore with our hiring team what the opportunities are.

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