Staff Decision Scientist, Advanced Analytics

Life360

2h • $130,500 - $242,500 • Remote

About The Position

Life360 is hiring a Staff Decision Scientist to lead Advanced Analytics across the Data & Analytics organization, focusing on decision science and data science within MAU. The role works across Operating Groups rather than being embedded in one, setting the methods, models, and standards used by the broader data team. It reports into the Data & Analytics leadership team.

The position owns Life360’s MAU forecast and growth model, which leadership uses for business planning and target-setting, and raises the standard for experimentation and causal inference. The role partners with Decision Scientists, Data Scientists, Analytics Engineers, and Data Engineers to make advanced methods repeatable and durable, and uses AI tooling to scale analysis across the team. It is not a production machine-learning role.

Success in the first year includes adoption of an experimentation and causal-inference standard across the team, and an MAU forecast with tracked accuracy that leadership uses for planning. The role is remote-first and may be performed remotely within the US and Canada. US salary range is $130,500–$242,500 USD; Canada salary range is $180,500–$214,000 CAD. Total compensation includes Restricted Stock Units and a comprehensive benefits package.

Requirements

• At least 8 years in an analytics, data science, or decision science role at a consumer technology company.
• An advanced degree in a quantitative field such as economics, statistics, quantitative social science, or operations research, or equivalent practical experience.
• Deep applied experience with causal-inference methods, such as difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, or propensity score matching.
• Experience building and owning a forecast or growth model for a core business metric, using 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 such as Statsig, Optimizely, or similar.
• A track record of setting analytical standards and mentoring other data scientists.
• Proficiency in SQL and Python or R for statistical analysis.
• Strong methodological judgment, including understanding methods’ assumptions, limitations, and when a simpler approach is preferable.
• Ability to own forecasts and standards end to end, track their performance, and improve them when needed.
• Use of AI tools such as Claude Code or equivalent as a development partner, with critical review of outputs and the ability to scale workflows across a team.
• Ability to teach and influence teams without direct authority, and to communicate methods and findings clearly.

Nice To Haves

• Experience with subscription or freemium business models.
• Background in growth, retention, or lifecycle analytics.
• Experience with Bayesian methods or probabilistic programming, such as PyMC or 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 or multi-market analytics.

Responsibilities

• Own and maintain the MAU forecast and growth model in Python and SQL, breaking MAU into registration, retention, and reactivation; document assumptions, track forecast accuracy, explain misses, and support leadership planning and target-setting.
• Establish standards for experiment design and analysis, including power, metric selection, variance reduction, sequential testing, heterogeneous effects, and interference; review high-stakes experiments.
• Advance the causal-inference toolkit by selecting appropriate quasi-experimental methods when randomized experiments are not possible and turning one-off analyses into reusable approaches.
• Define and document methodological best practices, review analyses across Operating Groups, and advise on difficult methods questions.
• Collaborate with Operating Group-embedded Decision Scientists, Data Scientists, Analytics Engineers, and Data Engineers to address difficult problems and create durable data models, metrics, and tools.
• Use AI workflows, templates, and tools to make forecasting, experiment readouts, and causal analyses faster and more consistent; identify repeated analyses for automation.
• Mentor colleagues and improve analytical practice through analysis and code reviews, internal talks, and written guides.
• Translate forecasts and causal findings into clear recommendations for leadership.

Benefits

• Medical, dental, vision, life, and disability insurance plans; the listed plans are 100% paid for US employees. Canadian employees receive life and disability coverage and supplemental medical and dental plans.
• 401(k) plan with company matching in the US; RRSP with DPSP plan for Canadian employees.
• Paid parental leave.
• Mental Wellness Program and Employee Assistance Program (EAP).
• Flexible PTO, companywide holidays, and summer and winter shutdowns.
• Learning and Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership and Tile trackers.
• Restricted Stock Units (RSUs).

What This Job Offers

• Job Type: Full-time
• Career Level: Senior