This role reports into the Head of Product and works closely with UX design, software and data engineering, science and customer success teams, and product marketing. This is an execution-first role with a clear growth path: as the platform scales and new use cases come online, there is scope to own a product surface end-to-end.

Responsibilities

Product Delivery (~70–80%)

• Translate roadmap priorities into detailed engineering requirements and acceptance criteria.
• Own the UX handoff process with the designer: polished interactions, resolved edge cases, defined state management.
• Manage sprint execution, track delivery against commitments, and proactively flag risks.
• Coordinate dependencies across engineering, science, and other teams.
• Run the operating cadence: standups, sprint reviews, status updates, and launch readiness.
• Manage prioritization of customer requests, providing transparency on timelines and decisions to the customer success team and other stakeholders.

AI-First Processes (~20–30%)

• Introduce AI prototyping and automation tools into the product development workflow.
• Identify high-impact manual workflows and build proofs of concept for agentic workflows.
• Drive change management for AI adoption across the product team.
• Be a credible discussion partner with engineering on AI/agentic capabilities for the product itself.

Job requirements

• End-to-End Product Delivery: You have owned delivery end-to-end in a product organization, from requirements gathering and definition, through backlog management, UX handoffs, and UAT.
• Engineering and Science Partnership: You have experience working alongside software and data engineering teams, as well as science teams.
• Decision Making: You are comfortable making product decisions at your level, with the maturity to know when to escalate critical blockers, and can manage complex tradeoffs between functionality, UX, and customer value.
• Basic Technical Literacy: You have a basic understanding of software development building blocks and machine learning concepts, allowing you to speak the engineering and science team's language with some guidance.
• Ownership & Validation: You have a strong sense of ownership and the ability to propose and validate ideas using a balanced combination of data and sound judgment.
• Prioritization: You use established prioritization frameworks to manage requests from multiple customers, ensuring clear and rapid communication between product, engineering, and customer success teams, and feel comfortable saying “no” while keeping stakeholders' trust.
• AI-Native Execution: You have hands-on experience building with AI tools (prototyping, coding, agents, automation) and can identify where AI can drive innovation, accelerate decision-making, or automate processes.

Nice to Have

• Advanced Technical Fluency: Experience in data-heavy or technically complex products – ML pipelines, geospatial data, satellite imagery, scientific computing – with a real understanding of system development, able to engage in highly technical discussions with engineering or science teams and propose solutions, not just requirements.
• Change Management: A track record of driving process or behavior change in an organization, with a specific example of moving people from old ways of working to new ones, especially when they didn't initially ask for it.
• Advanced AI Builder: Experience creating multiple complex applications using state-of-the-art AI practices and tools, able to move from idea to high-fidelity prototype (API integration, rich visualizations, etc.) in hours or days, with a strong point of view on when to use AI and when not to.
• B2B SaaS Expertise: Experience in B2B SaaS, especially platforms serving multiple use cases or customer segments on shared infrastructure.
• Domain Experience: Experience in AgTech, environmental tech, precision agriculture, or an adjacent domain where domain expertise and technical depth intersect.
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