Senior Product Manager, Zeitview

• Bachelor's degree (or higher) in a science, technology, or related field, or equivalent experience
• 8+ years of professional experience, with 5+ years in product management
• You have built and shipped enterprise B2B SaaS products. Not just managed a backlog, but made architectural and scoping decisions that affected whether the product worked at scale
• You have hands-on experience with AI/ML-powered product capabilities: generative AI, LLMs, agent-based systems, or comparable. You can evaluate an AI feature's readiness for production, not just pitch one
• You can hold your own in technical design discussions with engineering leads. You've shaped system architecture, evaluated build-vs-buy trade-offs, and ensured prototype-to-production paths are viable
• You have delivered complex, ambiguous programs across multiple teams where no single person had full authority, coordinating through influence, clarity, and follow-through
• You can write a product requirement that an engineer reads once and builds from. Your specs reduce ambiguity rather than create it
• You are fluent in quantitative reasoning: you've built measurement frameworks, designed experiments, and made judgment calls when the data was incomplete

Desirable

• Build the core product that serves every Zeitview vertical
• You will build and ship Zeitview's primary enterprise platform, Asset Insights
• You receive problem definitions and prioritized requirements from our vertical product leads: PMs who sit with customers in solar, wind, utilities, oil & gas, and commercial properties, and you turn those into working software
• You partner with engineering leads to shape technical approach, manage cross-stage dependencies across the inspection pipeline (capture → analysis → delivery), and deliver complex programs at quality
• Product vision and strategy are set by the Head of Product and VPs of Product; your job is to contribute technical judgment and then execute
• What you'll actually do in the first 6 months:
• Evaluate where generative AI, LLM, or agent-based capabilities should replace or augment existing platform workflows, and where they shouldn't
• Negotiate scope trade-offs between multiple vertical PMs competing for the same engineering capacity, and make those trade-offs legible to leadership
• Own the backlog for one or more pipeline stages in Asset Insights (e.g., analysis, analytics, action), writing epics and stories that engineering can pick up without a follow-up meeting
• Work side-by-side with operations teams who run inspections and deliver reports, identifying where product investment eliminates manual steps
• Inherit and improve the interface contracts between pipeline stages so that a change in capture doesn't break delivery
• Stand up or refine measurement frameworks for your product areas, defining what "working" means before you ship, not after