Manager, Product Analytics & Data Science
New
C
CompanyCamSaaS, Technology
You must live and work permanently in the U.S. to be considered.Full-TimeManager
Salary160,000 - 176,000 USD per year
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Job Details
- Experience
- 5–8 years
- Required Skills
- SQLMachine LearningProduct AnalyticsData scienceA/B testingdbtLooker
Requirements
- 5–8 years of experience in product analytics, data analytics, or data science
- At least 2 years in a people leadership or team lead role
- Experience leading analytics or data science work within SaaS or technology environments
- Strong SQL proficiency and experience working with complex data models and analytical workflows
- Experience with experimentation frameworks, A/B testing, and causal inference methodologies
- Working knowledge of machine learning concepts, predictive modeling, and how data science supports product development
- Experience with modern analytics and semantic layer tooling such as dbt, Metabase, Secoda, Looker, or similar platforms
- Strong communication and stakeholder management skills
- Experience managing cross-functional projects and balancing competing priorities
Responsibilities
- Lead and develop a team of Product Data Analysts and an embedded Data Scientist through coaching, prioritization, and hands-on analytical leadership
- Establish clear expectations and quality standards for product analytics, experimentation, and data science work
- Build scalable operating processes for intake, prioritization, stakeholder communication, and cross-functional collaboration
- Partner with Product and Engineering leaders to support data-informed product strategy and decision-making
- Guide experimentation strategy and ensure rigorous A/B testing methodologies and actionable interpretation of results
- Improve product metrics and scorecards that measure product health, workflow usage, launch performance, and customer outcomes
- Enable thoughtful self-service analytics through tools such as Metabase, Amplitude, Secoda, and dbt
- Translate complex analysis and data science work into clear, decision-oriented insights for technical and non-technical stakeholders
- Identify practical, high-impact opportunities for predictive modeling, classification, recommendation systems, and AI-enabled product intelligence
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