Product Manager, QuantSpark

• AI is a vital part of how we work here - we're less interested in whether you've used it, and more interested in what you've done with it, so we'll be curious to hear how it shows up in your own day to day
• If you've got consulting experience, or you've owned external client relationship before, you'll feel at home fast
• Real external client ownership. External client account relationships, with commercial risk attached
• Confident, clear communication. You can hold a room of senior stakeholders and communicate clearly with gravitas
• A track record with AI tools. If you've prototyped something in Claude Code or Cursor to unblock a decision or bypass an engineering queue, tell us about that!
• Consulting or client-facing experience. You'll thrive here if you've worked in a consulting environment, or you've owned client-facing work elsewhere
• Comfort with pace and autonomy. You've thrived somewhere fast-moving, whether that's a smaller business, a consultancy of any size, or a high-growth environment
• Commercial acumen. You understand how product calls connect to business outcomes

Desirable

• You'll move across a portfolio of client engagements spanning our four capabilities, some short and exploratory, some longer and more complex
• You'll own the roadmap, run discovery, manage senior client relationships, and prototype directly using AI tools to validate ideas before we commit engineering time to them
• We're looking for someone who can run delivery with real autonomy today, who will hold a project team together day to day
• Own the client relationship. Manage expectations, communicate progress, build trust with senior stakeholders, and hold your ground when it gets commercially tense
• Shape the product. Define vision, translate strategy into a prioritised roadmap, and make the trade-off calls that come with it
• Run agile delivery. Lead ceremonies, manage the backlog, write acceptance criteria, keep the team focused on what actually matters
• Lead discovery. Design and run stakeholder interviews and workshops that get to the real problem, not just the first one mentioned
• Prototype, don't just spec. Use AI tools to build functional prototypes and test assumptions before engineering resource gets committed
• Do the business analysis. Turn messy requirements into clear specs, and push back on both clients and engineers when the wrong problem is being solved
• Keep multiple workstreams moving. Coordinate cross-functional delivery, on time, within scope, without dropping anything
• Land the change. Lead training and adoption work so clients actually get value from what we've built, not just a product that sits unused