Senior Technical Product Manager

Your Opportunity Founded in 2024 our client is a seed-stage startup with a pioneering approach to wildfire prevention, leveraging novel, predictive models to prevent catastrophic wildfires ignited by lightning over (and near) high-risk areas. Lightning strikes account for 60% of wildfires in Canada, resulting in 93% of the burned area and emissions and their technology focuses on reducing wildfire occurrences and emissions by suppressing lightning strikes before they ignite these fires. Their work combines cutting-edge geospatial data analysis, machine learning, and computer vision to create a first-of-its-kind solution that anticipates and prevents lightning-induced wildfires at their source. This is a rare opportunity to build entirely novel capability and to contribute to a critical area of research that’s largely uncharted. As a Senior Technical Product Manager, you will help translate scientific research, machine learning development, and cloud-based engineering into a clear, production-focused roadmap that leadership and external stakeholders can rely on for planning and execution. The work connects applied research, software delivery, and commercial readiness into a single, visible operating model. As the company scales its platforms and enterprise-facing systems, you will establish the execution frameworks, dependency management practices, and communication layers that allow complex technical progress to be understood, sequenced, and delivered with confidence. Key Responsibilities Execution framework ownership: Establish and maintain progress tracking systems and dashboards that provide leadership with clear visibility into development velocity and milestone delivery on a recurring basis Requirements translation: Partner with business and commercial stakeholders to convert customer needs and market signals into well-defined technical requirements for engineering and data teams Executive partnership: Serve as a technical advisor to senior leadership, communicating tradeoffs, risks, and timelines in a way that supports go-to-market planning and decision-making Architecture alignment: Collaborate with engineering teams to ensure cloud-native systems and data platforms can support large-scale data processing and model deployment Infrastructure strategy support: Contribute to the evolution of machine learning service infrastructure to meet performance, reliability, and scalability needs for enterprise-facing use cases Dependency management: Coordinate the lifecycle of interconnected data pipelines, models, and services, ensuring upstream and downstream changes are communicated and integrated across teams Sprint and ritual leadership: Lead day-to-day execution practices, ensuring work is well-scoped, documented, and tracked through consistent engineering workflows Cross-team orchestration: Manage critical paths across data engineering, machine learning, and cloud infrastructure to prevent delivery blockers…