Product Manager, Data Review

Responsibilities

• Ideate and synthesize evidence: Work with customers and internal stakeholders to gather product ideas and evidence for investment. Apply rigor to the question of what to build, considering existing customer pain, potential markets, engineering effort, opportunity cost, and design cohesion.
• Collaborate with engineering and design: Partner with engineering and design to turn ambiguous problems into clear requirements and milestones. Recommend pragmatic scope/quality tradeoffs, unblock execution, and close the loop post-launch with feedback and followups.
• Be fluent in the details: Work closely with your engineering and design pod, and coordinate directly with the customer-facing engineers who use your product area day to day. Learn and understand the complexities so you can provide a direction that really works.
• Communicate with clarity and purpose: Identify the most important thing and hone it relentlessly. Produce documents and decks, prototypes that align stakeholders and point toward proven value.
• Build product systems: Create a product that generalizes across diverse hardware orgs, empowering the mission of one customer while contributing to systems to serve 100s more.
• Ship solutions: Ship product that accelerates hardware testing.

Requirements

• 3–5 years of product management experience, including at least one 0→1 launch and a track record of shipping meaningful increments, ideally on an early-stage product built for technical users.
• Genuine excitement about how software is transforming the way complex hardware systems get tested and validated.
• Ability to take a loosely defined problem space, like "how engineers root-cause an anomaly," and turn it into a concrete plan.
• Strong cross-functional partnership with engineering and design, with a knack for aligning stakeholders, including without direct authority.
• Ability to discuss system design, data modeling, and APIs at a working level.
• Excitement about AI agents as a product paradigm, with a clear point of view on where agents create durable advantage versus commoditized features.
• Ability to synthesize complex topics for operators, engineers, and executives alike, in writing and in conversation.
• Demonstrated bias toward shipping, learning, and iterating quickly, even under real constraints.

Nice-to-Haves

• Experience in hardware engineering or adjacent technical field.
• Experience working in a DoD context with a security clearance.
• Experience in product design and an ability to produce prototypes to communicate ideas.
• Experience in software development either in a backend, frontend or edge context.
• Experience with hardware data review frameworks like FMEA, Bayesian RCA or Fishbone.
• Experience with data analytics or data science especially if applied to hardware data.
• Experience with data visualization time series analysis, or telemetry systems.