• Drive Device growth through rigorous, data-driven insights that deeply connect product, pricing, and customer behavior. • Translate complex analyses into clear, actionable recommendations for senior leadership. • Establish and govern a unified metrics framework across Devices and Monetization, defining canonical KPIs. • Champion a culture of experimentation by designing and analyzing A/B tests and quasi-experiments that unlock growth, improve conversion, and enhance user experience. • Ensure cross-functional alignment on metric definitions, and maintain trusted source-of-truth datasets for experimentation and executive reporting. • Lead pricing and experimentation initiatives, including price point testing, promotions, bundling, and subscription attach optimization. • Quantify incremental revenue, margin impact, and long-term LTV effects. • Build and maintain price-demand elasticity models to inform pricing, promotional, and portfolio decisions. • Apply Bayesian experimentation and hierarchical modeling techniques, particularly in low-traffic environments (e.g., new hardware launches, geo pilots), to improve speed and confidence of decision-making. • Use causal inference and econometric methods to simulate revenue and contribution margin under different scenarios. • Develop scalable monetization frameworks (e.g., Devices LTV, cohort profitability models) to support sustainable growth and margin expansion. • Partner with Data Engineering to improve data quality, experimentation infrastructure, and self-serve analytics capabilities across the organization. • Serve as a thought leader in Analytics, elevating statistical rigor and advancing Life360’s data-driven culture internally and externally.

• Support management in the development and execution of a BI and reporting strategy aligned with organizational goals to drive growth, profitability, and operational efficiency. • Partner with departments such as Claims, Underwriting/Operations, and Product Management to identify, assess, and prioritize business intelligence needs. • Design, develop, and deliver management dashboards and key performance indicators (KPIs) that provide actionable business insights. • Ensure all reports and dashboards support business users' decision-making process. • Work alongside other analytical teams to build and maintain reliable analytical datasets that support broader business goals. • Drive enhancements in reporting processes and data quality to deliver accurate, timely, and actionable insights for decision-making. • Perform quarterly and annual regulatory statistical reporting to ensure compliance and accuracy.

• Collaborate closely with one of our product teams to collect and analyze data that helps us build the best experience for our customers. • Use structured and unstructured data to understand customer problems and behavior through statistical analysis and data visualization. • Conduct ad-hoc analyses and build data tools, such as dashboards and reports, to enable ongoing data exploration and improve our data infrastructure.

• Gather and understand business needs and objectives. • Identify and interpret patterns, trends, insights, and business metrics from data • Create data-complex dashboards with an optimized data structure and scaling perspective • Assist in planning, forecasting, and AB testing processes. • Work with various product and business stakeholders to develop data-driven insights. • Partner with the Data Engineering team to define and build reliable data sources. • Identify new opportunities for the use of data across the company.