Senior Director, MAU Analytics and Data Science

About The Team

The Data & Analytics Team (DnA) is a 60+ person organization spanning data engineering, analytics engineering, analytics/decision science, and data science/ML. The science teams are organized into two sub teams, focusing on MAU and Revenue. This role leads decision science and data science within MAU.

You and your team will be responsible for understanding the drivers behind MAU: why members come, why they stay, and why they return. The team partners with product, marketing, and engineering to put those learnings into everyday work, and that work shapes our roadmaps.

Our data is unusual for a consumer company. Life360 measures families and circles rather than individuals, so one member's behavior affects another's, which makes measurement a different problem than a standard funnel. With billions of location events, driving behavior, crash detection, and Tile and Pet GPS trackers, we get a rich picture of physical behavior that few consumer companies can build.

About the Job

This Senior Director leads MAU Analytics and Data Science.

You and your team will work across acquisition, activation, engagement, and retention on the analysis, models, and experiments behind each. A lot of what this role owns needs to be built rather than maintained: the measurement, the standards, and how the team uses AI. The team is also raising its craft, and coaching people through that is an important part of the job. Within a year, your team members will be operating at an elevated skill level, proactively bringing key questions to light, and serving as trusted advisors whom stakeholders consult for strategic guidance rather than simple task execution.

You will report to the Head of Data and work closely with counterparts leading Revenue Analytics and Data Science, and with the leaders of Data Engineering.

What You'll Do

• Lead and develop the decision scientists and data scientists on the team, and set the quality bar for both disciplines.
• Own the MAU driver model and the forecast.
• Own the exploratory work that drives the strategy to grow MAU .
• Establish how an analysis gets scoped, run, reviewed, and written up, so its quality does not depend on who picked up the request.
• Partner with your Revenue counterpart and Data Engineering leadership to define common metrics, align on experimentation standards, and introduce unified operating practices across teams.
• Build the operating model that gets the team into the roadmap conversation early, with a view on what to build and what to test.
• Use agents and automated analysis daily, and expect the same of the team.
• Shape how the team works AI-native: what gets delegated to agents, what gets automated, and what stays human judgment.

What We're Looking For

Required Experience & Qualifications

• 10+ years in analytics, data science, or decision science, including 5+ years in people management in a senior leadership role.
• Deep expertise in inferential and causal analytics. Able to lead ML practitioners.
• Expert knowledge of engagement, retention, churn, and cohort metrics.
• Experience forecasting a company-level metric and being held to it.
• SQL , plus Python or R .
• Experience with Databricks , third party experimentation tools (e.g. Statsig ), Tableau or similar BI tools, and AI native BI tools (e.g. Hex , AI/BI ).
• Hands-on with coding agents ( Claude Code or equivalent) in your own analytical work.
• Bachelor's degree or equivalent.

Nice to Have

• Familiarity with experiment design to identify and control for network effects.
• Master’s degree or PhD with an emphasis in social science, psychology or other behavioral science.
• Experience leading a team that reports to you but sits embedded with product and engineering teams.
• Experience in a B2C subscription business .

Core Expectations

• You have moved an analytics team from reactive to embedded.
• You coach and develop people, and you have grown analysts into leaders.
• On your teams, analysis starts from a hypothesis rather than a data request.
• You have built the standards a team works from, and got people to use them.
• You are curious about why people behave the way they do, not just what they did.
• You decide what the team does not work on, and you can defend it.