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 and maintain development momentum.
- Resource and priority sequencing: Support leadership in making informed decisions about technical investments, tradeoffs, and the ordering of major initiatives
Tech Stack
- Data and ML systems: Large-scale data ingestion pipelines, machine learning pipelines, model deployment infrastructure
- Program and delivery tools: Engineering ticketing systems, dashboarding and reporting tools, sprint and workflow management platforms
Your Know-How
- 5+ years owning product delivery in deeply technical domains (data platforms, ML products, infrastructure-heavy systems, or developer/platform products)
- You have led or managed programs involving large-scale data platforms, machine learning systems, and cloud infrastructure and are able to translate complex technical and scientific work into clear, business-focused updates for senior stakeholders and leadership
- You can operate in early-stage ambiguity while building the foundations enterprise customers expect around delivery, documentation, reporting, executive communications and operational maturity
- You can reason about system design, data flows, and ML lifecycle enough to ask sharp questions, spot gaps, and drive clarity
- You have experience translating ML work into product outcomes, including understanding datasets, model iteration cycles, evaluation metrics, monitoring, model drift, and the realities of deploying and operating ML in production
- You are comfortable working with cloud-native architectures and the tradeoffs involved in scaling ingestion, compute, storage, latency, reliability, and cost
- You create crisp requirements that reduce ambiguity (well-scoped epics, user stories, acceptance criteria) and definitions of done that teams can actually execute against
It’s a Bonus If
- You have experience working with data collected from sensor-based, geospatial, or field-deployed systems
- You have worked with (or adjacent to) atmospheric modeling, forecasting systems, or high-stakes environmental prediction
- You have experience supporting government customers, utilities, or regulated enterprises, including longer buying cycles, compliance requirements, and high standards for documentation and auditability
- You owned the implementation of reporting frameworks for board-level and executive-level technical visibility