Product Owner, Holded

• You have solid experience as a Product Owner or Product Manager in a B2B SaaS environment
• You genuinely understand AI — not as a buzzword, but at a level where you can evaluate feasibility, challenge assumptions, and have
• An informed conversation with engineers about what LLMs, automation, or AI-assisted features can and can't do
• You have worked on or shipped AI-powered features and can speak to what worked, what didn't, and why
• You are deeply user-centric: you talk to users regularly and let real problems drive your roadmap
• You are data-informed: you know how to define the right metrics, interpret them, and act on them
• You write with clarity — your specs, stories, and decisions leave no room for ambiguity
• You are comfortable in a fast-paced environment and can manage trade-offs without losing sight of the bigger picture
• You can have a technical conversation with engineers without needing to be one yourself
• You are proactive, direct, and comfortable pushing back when the evidence supports it

Desirable

• As a Product Owner, you will develop the product vision by working closely with C-level and stakeholders and transmit it to our skilled engineering team. Your main responsibilities will be:
• Take over the roadmap, decisions and learnings from the outgoing PO, and shape your own read of the product state in your first weeks
• Own the strategy and roadmap of the CRM & Projects module, balancing adoption improvements, competitive gap closing, and AI integration
• Prioritize the backlog and manage trade-offs between new features, experience improvements, product debt and progressive AI capability integration
• Lead discovery through customers and data: interviews, quantitative analysis, prototyping, and A/B experimentation
• Partner daily with an existing cross-functional squad (engineering, design, data) in an agile setup, refining user stories and accepting deliverables
• Define success metrics (adoption, retention, business impact) and AI-specific quality metrics (accuracy, latency, human fallback)
• Design experiences that hold up against the probabilistic nature of AI outputs: fallbacks, edge cases, and user expectations