Staff Decision Scientist, Advanced Analytics

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), as of June 30, 2026, across more than 180 countries.  

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above. 

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. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

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.

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 we run experiments and establish causality, and owning the MAU forecast and growth model that leadership uses to plan the business. You'll report into the Data & Analytics leadership team.

Most of your impact will come through other people. You'll partner closely with Decision Scientists, Data Scientists, Analytics Engineers, and Data Engineers, and you'll use AI to make advanced methods repeatable, so rigor doesn't depend on whether you personally touched the project. 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 the judgment to know which method fits the question.

In your first year, success looks like an experimentation and causal inference standard the team has adopted, and an MAU forecast with tracked accuracy that leadership uses for planning.

For candidates based in the US, the salary range for this position is $130,500 to $242,500 USD. For candidates based out of Canada, the salary range for this position is 180,500 to 214,000 CAD. We take into consideration an individual's background, job-related knowledge, skills, and experience in determining final salary. Base pay may also vary based on geographic location, with US work locations falling into one of three geographic tiers.

Total compensation is inclusive of equity compensation in the form of Restricted Stock Units (RSUs), as well as a comprehensive benefits package, including medical, dental, vision, financial, and other benefits. 

• Own the MAU forecast and growth model. Build and maintain the model (Python, SQL) that breaks MAU into registration, retention, and reactivation, and that leadership relies on for planning and target-setting. Make the assumptions explicit, track forecast accuracy, and explain the misses.
• Raise the bar on experimentation. Set the standard for experiment design and analysis: power, metric selection, variance reduction, sequential testing, heterogeneous effects, and interference in a product where users share Circles with family and friends. Be the reviewer the team seeks out for high-stakes tests.
• Push our causal inference toolkit forward. When a clean experiment isn't possible, bring the right quasi-experimental method (difference-in-differences, synthetic controls, regression discontinuity, instrumental variables, and others) and turn one-off analyses into reusable approaches others can apply.
• Set the methodological standard for the data team. Define and document best practices, review analyses across OGs, and be the go-to person for hard methods questions.
• Collaborate across disciplines. Work with OG-embedded Decision Scientists on their toughest problems, with Data Scientists on modeling approaches, and with Analytics Engineers and Data Engineers to turn methods into durable data models, metrics, and tooling.
• Scale advanced analytics through AI. Build agent workflows (Claude Code or equivalent), templates, and tools that let the team run forecasts, experiment readouts, and causal analyses faster and more consistently. Treat every repeated analysis as a candidate for automation.
• Teach and mentor. Level up the craft across the org through mentorship, analysis and code reviews, internal talks, and written guides. Raise the ceiling for senior ICs and the floor for everyone else.
• Make complex methods land with leadership. Translate forecasts and causal findings into clear recommendations with a point of view. A rigorous result nobody acts on is a failed result.

• Methodological depth with judgment: You know the assumptions behind each method, when they break, and when a simpler approach is the better answer.
• Ownership mentality: You own the forecast and the standards end to end, including tracking whether they hold up and fixing them when they don't.
• AI-native working style: You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams. You think about how AI can scale the work of the whole team, not only your own.
• Multiplier mindset: You measure your success by how much better the team's work gets. You enjoy teaching and you're patient with people learning new methods.
• Influence without authority: You've driven adoption of standards and methods across teams you don't manage by making the case clearly and making the right way the easy way.

• 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 (e.g., difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, propensity score matching).
• Experience building and owning a forecast or growth model for a core business metric (e.g., MAU, DAU, subscribers, revenue), including cohort- or component-based approaches.
• Expertise in experiment design and analysis at scale, including variance reduction, sequential testing, and network effects or interference.
• Experience with experimentation platforms (Statsig, Optimizely, or similar).
• Track record of setting analytical standards and mentoring other data scientists.
• Proficiency in SQL and Python/R for statistical analysis.

• 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 building dbt models or contributing to analytics engineering workflows.
• Familiarity with international / multi-market analytics.

• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer life and disability as well as supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Paid parental leave.
• Mental Wellness Program & Employee Assistance Program (EAP) for mental well-being.
• Generous time off: flexible PTO, companywide holidays throughout the year plus summer and winter shutdowns.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership and Tile trackers.

AI Usage: We use AI to help make our hiring process efficient. That said, every hiring decision is made by real Life360 hiring teams

Our company’s mission-driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference 

• Be a Good Person - We have a team of high integrity people you can trust. 
• Be Direct With Respect - We communicate directly, even when it’s hard.
• Members Before Metrics - We focus on building an exceptional experience for families. 
• High Intensity, High Impact - We do whatever it takes to get the job done. 

Life360 is an equal opportunity employer. We don’t discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other legally protected status. We believe diverse perspectives make us better, and we’re building a workplace that reflects the communities we serve. Don’t meet every qualification? Apply anyway.

#LI-Remote

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