Product Owner

Responsibilities:

• Own and groom the product backlog for a scientific/ML engineering team, translating research-stage work into features that are estimable and sequenced without stripping out genuine ambiguity.
• Work closely with the research and engineering team to understand their workflows and pain points well enough to represent them accurately in product decisions.
• Build and defend a prioritization approach, starting with a foundational model for the initial release and evolving it as real usage data comes in.
• Balance long-term roadmap thinking with the immediate needs of the team and customers, navigating ambiguity without losing sight of the bigger picture.
• Set up and run the team's working process and tracking, comfortably building the system rather than inheriting one.
• Take on defined market research activities such as competitive landscape scans, customer and segment validation, and positioning input, feeding directly into roadmap decisions.
• Produce backlog and roadmap artifacts that are self-documenting and usable by both the technical team and cross-functional stakeholders.

Requirements:

• 5-8 years in a product owner or hybrid product owner/product manager capacity.
• Product Owner/Manager experience in a B2B enterprise software product, not a consumer or internal-tools context.
• A deep understanding of how engineers or technical researchers actually work: their workflows, their pain points, and what done looks like from experimentation through deployment.
• A structured, strategic mindset, with the ability to navigate ambiguity, drive alignment, and deliver value quickly without losing sight of long-term goals.
• Direct experience with on-premises or hybrid deployment models and the constraints these place on architecture, rollout, and customer expectations beyond pure SaaS.
• Exposure to PLM (Product Lifecycle Management) ecosystems or workflows, and familiarity with how engineering data and processes are governed in that world.
• A bias toward action, with a track record of resolving blockers and iterating quickly in a fast-moving environment.

Good to Have / Bonus Points:

• Experience building products for technical users such as ML engineers, data scientists, or applied researchers.
• Background in AI platforms or scientific computing environments.
• Exposure to CAE or 3D visualization tools within engineering software ecosystems.
• Familiarity with data governance, sovereignty, or compliance considerations in enterprise B2B products.
• Enthusiasm for product management as a craft, with clear opinions on how to build successful products.