Product Operations Manager
New
S
SamsaraInternet of Things
Remote - Canada; Remote - USFull-TimeManager
Salary$106,802.50 — $179,500 USD
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Job Details
- Experience
- 1–5 years
- Required Skills
- Data AnalysisProduct OperationsBusiness OperationsProduct AnalyticsData analytics
Requirements
- 1–5 years of professional experience in a role such as product operations, data or product analytics, business operations, consulting, technical product management, or another environment where you solved ambiguous problems with data and technology.
- A demonstrated record of building and shipping something useful for real users—for example, an internal tool, application, dashboard, automation, data product, or repeatable analytical workflow.
- Experience structuring and analyzing messy or large datasets, validating the results, and explaining what the analysis does and does not prove.
- Strong product and customer judgment: you start with the user problem and business outcome, not the technology.
- Clear written and verbal communication.
- High ownership, curiosity, and attention to detail.
- Experience in a B2B technology company or working closely with Product and Engineering teams is preferred.
- Experience in Product Operations functions, working on defining and tracking product roadmaps, feature requests, and bringing the voice of the customer to every product conversation is preferred.
Responsibilities
- Build and ship internal data and AI products that help Product and Engineering teams understand product usage, customer friction, and the highest-impact opportunities to improve.
- Analyze large volumes of structured and unstructured data—including product telemetry, support tickets, customer conversations, feature requests, surveys, and product metadata—to find patterns that would be difficult to see manually.
- Turn one-time analyses into reliable, repeatable tools and workflows with clear data definitions, quality checks, documentation, and feedback loops.
- Translate complex findings into clear, decision-ready recommendations, while preserving the evidence and methodology behind them.
- Own projects end to end: scope the problem, identify and connect the right data, build the first version, launch it, measure adoption and quality, and continue improving it.
- Use AI-assisted development tools thoughtfully—checking outputs, debugging failures, and applying sound judgment rather than treating generated work as automatically correct.
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