Salsify
Senior Product Manager - AI Innovation

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Senior Product Manager - AI Innovation
As a Senior Product Manager on an AI Innovation team, you'll own the vision and strategy for a body of work at the intersection of AI and our core PXM platform. This is a building role: you're expected to move fast from idea to shipped value, experimenting and prototypes alongside the team. You'll frame strategic options for leadership, run discovery on problems that don't have an obvious answer yet, and partner closely with your engineering and design counterparts (your "trio") to bring things to life. You'll also build credibility with Customer Success, Sales, Marketing, Solutions Advisory, Support, and R&D so your releases initiates are understood and adopted across the org.
This role sits within Salsify’s AI Innovation group, so genuine curiosity about AI matters, along with a track record of turning emerging technology into meaningful customer value.
Responsibilities
- Set and champion the vision and strategy for your AI Innovation area, tied explicitly to Salsify’s business objectives; moving quickly and adapting as you learn, while articulating a clear and compelling vision for the future.
- Lead discovery with customers, partners, and internal stakeholders to uncover underlying use cases, needs, and business outcomes.
- Use AI to speed up and scale signal collection, pairing it with traditional discovery methods like customer interviews, competitive intelligence and usage analytics.
- Hands-on AI fluency: direct experience building with LLMs, agents, and AI tools.
- Run tight experiments and pilots: define hypotheses and success metrics, test in small increments, and own decisions about what scales, holding your team accountable to shipping value quickly.
- Partner with your engineering manager and designer to run the trio effectively, and step in when that partnership isn't working.
- Build and maintain relationships with Customer Success, Sales, Marketing, Solutions Advisory, Support, and R&D to keep AI Innovation work aligned with the rest of the organization.
- Identify synergies and risks across teams before they become problems, proactively solve across team leads and bring recommendations to the people who need to decide.
- Communicate status, progress, and blockers to senior leaders, and present to internal and external audiences, tailoring the message for each.
- Work through disagreement and misalignment directly and constructively, building trust along the way.
- Act as the face of your product area, internally and with customers.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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Requirements


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- 5-8 years of product management experience, or equivalent experience owning outcomes in an ambiguous, fast-moving environment.
- Demonstrated product judgment: you can frame strategy, weigh trade-offs, and defend a point of view to senior leaders.
- Hands-on AI fluency: direct experience with LLMs, agents, and AI tools. You have real experience building AI products, leveraging AI to optimize your output and promoting AI ways of working across your team.
- Experimental discipline: comfortable defining hypotheses, setting success metrics, running pilots, and making clear, data-driven calls from the results.
- A track record of leading discovery with technical audiences and distilling ambiguous feedback into a clear plan.
- Experience prioritizing across competing time horizons, with the judgment to say no to work that doesn't serve the objective.
- Communication that bridges audiences: equally effective with engineering teams and business stakeholders, and comfortable presenting to both internally and externally.
- A history of landing cross-team or cross-company initiatives that moved a company-level metric.
- Resilience and a bias to action in ambiguous situations, with the ability to create clarity for the people around you.
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