Staff Decision Scientist, Advanced Analytics - Life360

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 102.4 million monthly active users (MAU) across more than 180 countries.

Life360 is a Remote First company, meaning a remote work environment is the primary experience for all employees (performable remotely within the US and Canada).

AI-Native Culture

We are building an AI-native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role; clear guidance will be provided by your recruiter.

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 focus 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 shape business roadmaps based on billions of physical location events, driving behavior, crash detection, and tracking devices.

About the Job

We're hiring a Staff Decision Scientist to lead Advanced Analytics: the methods, models, and standards the rest of the data team builds on. You won't be embedded in a single Operating Group (OG)—you'll work across all of them, pushing how experiments are run, establishing causality, and owning the MAU forecast and growth model that leadership uses to plan the business.

This is not a production ML role (we have a dedicated Data Science / MLE team for that). Your primary tools are statistical inference, forecasting, clear thinking, and methodological judgment.

What You’ll Do

• Own the MAU forecast and growth model: Build and maintain the model (Python, SQL) breaking MAU into registration, retention, and reactivation for leadership planning.
• Raise the bar on experimentation: Set standards for experiment design and analysis, including power, metric selection, variance reduction, sequential testing, and interference in network-based product models.
• Push our causal inference toolkit forward: Apply quasi-experimental methods (difference-in-differences, synthetic controls, regression discontinuity, instrumental variables) and turn one-off analyses into reusable approaches.
• Set methodological standards: Define and document best practices, review analyses across OGs, and act as the go-to expert for complex methodologies.
• Collaborate across disciplines: Work with Decision Scientists, Data Scientists, Analytics Engineers, and Data Engineers to convert methods into durable data models and tooling.
• Scale advanced analytics through AI: Build agent workflows (Claude Code or equivalent), templates, and tools to run forecasts and causal analyses faster and consistently.
• Teach and mentor: Level up craft across the org through mentorship, code reviews, internal talks, and written guides.
• Communicate with leadership: Translate complex forecasts and causal findings into actionable recommendations with clear points of view.

What We’re Looking For

Core Expectations

• Methodological depth with judgment: Clear understanding of when methods break and when simpler approaches are superior.
• Ownership mentality: End-to-end ownership of forecasts and standards.
• AI-native working style: Leverage AI tooling (Claude Code or equivalent) as a development partner to scale team output.
• Multiplier mindset: Passion for teaching, mentoring, and elevating team performance.
• Influence without authority: Demonstrated success driving standards adoption across teams.

Required Qualifications

• 8+ years in an analytics, data science, or decision science role at a consumer tech company.
• Advanced degree in a quantitative field (economics, statistics, quantitative social science, operations research) or equivalent practical experience.
• Deep, applied experience with causal inference methods.
• Experience building and owning growth or forecast models for core business metrics (MAU, DAU, subscribers, revenue).
• Expertise in experiment design at scale (variance reduction, sequential testing, network effects).
• Experience with experimentation platforms (e.g., Statsig, Optimizely).
• Track record of setting analytical standards and mentoring data scientists.
• Proficiency in SQL and Python/R for statistical analysis.

Preferred Qualifications

• Experience with subscription or freemium business models.
• Background in growth, retention, or lifecycle analytics.
• Experience with Bayesian methods or probabilistic programming (e.g., PyMC, Stan).
• Experience with LTV modeling, incrementality testing, or marketing mix modeling.
• Experience building internal tooling or AI agent workflows for analytics teams.
• Experience with dbt models or analytics engineering workflows.
• Familiarity with international/multi-market analytics.

Benefits

• 100% company-paid medical, dental, vision, life, and disability insurance (US employees; supplemental/matched plans for Canadian employees).
• 401(k) match (US) / RRSP with DPSP plan (Canada).
• Paid parental leave.
• Mental Wellness Program & Employee Assistance Program (EAP).
• Flexible PTO, company holidays, plus summer and winter shutdowns.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for remote work.
• Free Life360 Platinum Membership and Tile trackers.