Manager, Product Analytics – Data Science

Role Overview

• 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
• Help teams connect product behavior to activation, adoption, engagement, retention, and customer value
• 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
• Partner cross-functionally with Analytics Engineering, Data Engineering, Engineering, and ML/AI teams to improve ownership clarity and data quality
• 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
• Help stakeholders distinguish between meaningful data science opportunities and lower-value or overly speculative work
• Contribute directly to high-priority analyses, ambiguous product questions, and strategic initiatives when needed
• Protect the team from low-leverage work while creating space for higher-impact analytical and strategic contributions

Requirements

• 5–8 years of experience in product analytics, data analytics, or data science, including 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 with the ability to simplify complex concepts for varied audiences
• Experience managing cross-functional projects and balancing competing priorities in fast-moving environments
• A continuous growth-mindset, with a focus on learning, embracing challenges, and continuously improving.
• A knack for creativity and innovation, bringing fresh ideas to the table and solving complex problems.

Tech Stack

• SQL

Benefits

• meaningful equity and other benefits