Senior Product Operations Manager

New

Based in United StatesFull-TimeManager

Salary not disclosed

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Job Details

Experience: 5–8+ years of experience in Product Operations, Product Analytics, Business Intelligence, or a related analytical role.
Required Skills: SQLBusiness IntelligenceProduct OperationsProduct AnalyticsSaaSLooker

Have 5–8+ years of experience in Product Operations, Product Analytics, Business Intelligence, or a related analytical role.
Bring a strong foundation in regression, correlation analysis, and predictive modeling.
Have experience contributing to or working within analytics or metrics programs, including helping build and evolve a program.
Translate complex data into actionable recommendations that influence product and business decisions.
Have strong SQL skills and proficiency with modern BI and analytics tools; Looker experience is preferred.
Be fluent with AI tools and able to incorporate them into research, analysis, synthesis, and workflow optimization.
Communicate analytical findings, business implications, and recommended next steps clearly to stakeholders.
Manage an independent workload and identify stakeholders who can act on insights.
Work in a highly organized, process-oriented way and collaborate across functions.
Travel for company events, new hire training, and team off-sites, typically 1–3 times per year.

Design and evolve a product metrics program with product leadership, connecting metrics to business outcomes.
Build and maintain outcome models linking product inputs to retention, revenue, customer health, and engagement.
Apply regression, correlation analysis, and predictive modeling to validate metric definitions.
Analyze customer behavior, product usage, friction points, and opportunities to improve business outcomes.
Translate findings into recommendations for product priorities, roadmaps, and OKRs.
Create data narratives for executive reviews, board presentations, product planning, and strategic decisions.
Develop dashboards, templates, and toolkits that enable teams to self-serve product insights.
Partner with Engineering and Data teams on event tracking, taxonomy standards, and data governance.
Train stakeholders on interpreting and applying product data.
Use AI tools to synthesize information, accelerate analysis, and scale analytical workflows.

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