Lead Decision Scientist

About the role

Life360 is seeking a Lead Decision Scientist to partner with our Direct To Consumer (DTC) web product, engineering, marketing, and finance teams. You will be instrumental in shaping product development, growth strategies, and investment decisions by providing analytical narratives and actionable insights. This role focuses on driving decisions, not just reporting data, and will contribute to our AI-native operational model.

Key facts

What you'll do

• Act as a strategic advisor to cross-functional teams, developing and championing web analytics roadmaps and identifying discrepancies between data findings and team assumptions.
• Communicate compelling narratives supported by data to influence team actions, analyzing user journeys from acquisition to web conversion to pinpoint drop-off points and opportunities for improvement.
• Establish causality through methods like A/B testing, analytics, and causal inference, and partner with teams to scale a statistically sound experimentation program.
• Collaborate with engineering on event instrumentation and with DTC teams to translate insights into actionable strategies for enhancing web metrics and conversion rates.
• Develop and implement measurement strategies that align with user experience and business objectives, analyzing consumer behavior to inform marketing, merchandising, and product strategies.
• Drive site conversion improvements through extensive A/B testing on product pages, checkout flows, and merchandising tactics.
• Integrate web event data from various platforms like Google Analytics and BigQuery into centralized data pipelines to support DTC reporting.
• Oversee the analysis of eCommerce KPIs, customer behavior, and digital marketing metrics, developing dashboards to highlight performance and identify optimization areas.
• Ground analysis in user motivations and context, providing explanations that go beyond raw data.
• Contribute to the vision for self-service and operational reporting using industry-leading tools.
• Utilize AI tools for analysis and contribute to shaping our AI-native analytics stack, moving towards proactive and autonomous operations.

Requirements

• Possess a problem-solving approach, meticulously structuring ambiguous challenges before applying any analytical tool.
• Demonstrate an ownership mentality, taking full responsibility for work from initial question framing to delivering recommendations and tracking impact.
• Embrace an AI-native working style, using AI tools as a development partner for delegating tasks, critically reviewing outputs, and running parallel workstreams.
• Exhibit curiosity and initiative, proactively exploring data to understand underlying mechanisms.
• Have 6 or more years of experience in analytics, data science, or decision science within a consumer product company.
• Hold an advanced degree in a quantitative field such as economics, statistics, quantitative social science, or operations research, or possess equivalent practical experience.
• Proven experience applying causal inference methods in real-world scenarios, including techniques like difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, or propensity score matching.
• A history of influencing product or business strategy with data, with concrete examples of cross-functional impact.
• Experience with experimentation platforms like Statsig or Optimizely.
• Proficiency in Google Analytics 4 (GA4) and/or Adobe Analytics is essential.
• Experience with data engineering to ingest web event streams into a central data environment.
• Strong proficiency in SQL and Python/R for statistical analysis.

Nice to have

• Experience with subscription or freemium business models.
• Familiarity with international or multi-market analytics.
• Proficiency in Google Tag Manager (GTM) or Tealium, with a strong preference for server-side deployment experience.
• Experience building dbt models or contributing to analytics engineering workflows.
• Experience with revenue forecasting frameworks for eCommerce businesses.
• Experience with LTV modeling, incrementality testing, or marketing mix modeling.

Skills & tools

• SQL
• Python/R
• Google Analytics 4 (GA4) or Adobe Analytics
• Causal inference methods
• Experimentation platforms (Statsig, Optimizely)
• Data engineering concepts
• AI coding agents (e.g., Claude Code)

Practical notes

• Salary range for US-based candidates: $133,000 to $195,000 USD.
• Salary range for Canada-based candidates: 147,500 to 173,000 CAD.
• Compensation includes base pay, equity, and a comprehensive benefits package.
• Benefits include medical, dental, vision, life, and disability insurance, 401(k) with company match (US) or RRSP with DPSP (Canada), Employee Assistance Program, flexible PTO, company-wide holidays, learning and development programs, remote work support, free Life360 Platinum Membership, and free Tile Products.
• Visa sponsorship is not provided for this role.
• Travel is not expected for this role.
• Application deadline is not specified.