Manager II, AI Product

About the Role

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.

Why This Role Exists

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.

Key Responsibilities

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.

What You Bring

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.

Skills Profile

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 sco