• support the Military Health System client by delivering advanced analytics, statistical modeling, backend application support, and enterprise reporting across MHS GENESIS and DHA data platforms • transform revenue cycle data into decision support intelligence that strengthens revenue surveillance, risk based monitoring, audit readiness, and Revenue Cycle Modernization and Innovation initiatives • Design and execute statistical and analytic methodologies to support revenue cycle surveillance, risk identification, remediation prioritization, and performance monitoring • Develop, optimize, and maintain advanced SQL and Vertica SQL queries, views, and reusable datasets supporting revenue cycle analytics including billing, collections, DNFB, denials, accounts receivable, write offs, and cash collections • Develop and maintain CCL and Discern queries and reusable extracts to support analytics, operational reporting, and backend diagnostics within MHS GENESIS • Access, extract, validate, reconcile, and integrate data from Cerner MHS GENESIS and DHA authorized enterprise repositories • Identify trends, anomalies, outliers, and revenue risk related to charge capture, registration, documentation, claim edits, denials, underpayments, and reimbursement performance • Perform data validation, lineage analysis, reconciliation, and quality assurance across multiple systems of record to ensure analytical accuracy and audit traceability • Conduct system impact assessments related to backend configuration, data flow, billing logic, and revenue cycle workflows in coordination with Oracle Health Cerner, DHA, CDAO, and RCMI technical teams • Investigate, triage, and support resolution of user reported issues impacting revenue cycle, scheduling, patient access, billing, and encounter workflows • Develop dashboards and analytic products using Power BI, Tableau, SAP Business Objects, and advanced Excel to support revenue cycle monitoring and executive reporting • Produce recurring and ad hoc analytic deliverables including executive summaries, methodology documentation, data dictionaries, and leadership briefings • Quantify financial impact of identified issues and support tracking of corrective actions through pre and post remediation analysis • Support testing, validation, release management, and interface changes to ensure analytic outputs align with system updates and configuration changes • Participate in incident, problem, and change management processes within IT service management frameworks • Collaborate with clinical, coding, billing, finance, IT, and operational stakeholders to translate analytic findings into actionable improvement strategies • Safeguard protected health information and comply with HIPAA, DHA cybersecurity directives, and federal data governance policies

• Own and maintain data relationships across finance, operations, commercial and exec teams to ensure data integrity and consistency • Develop and document data dictionaries, mapping business logic to underlying data sources • Review and work with Data Engineering to establish and enforce data quality standards, identifying and resolving discrepancies proactively • Help to manage integrations between core business systems (ERP, CRM, billing platforms) and reporting infrastructure • Maintain and enhance the business intelligence/visualisation platform (Gooddata), ensuring dashboards remain timely, accurate, and user-friendly • Build and refine automated reporting pipelines to reduce manual effort and improve timeliness • Troubleshoot data issues and respond to ad-hoc reporting requests from stakeholders • Prepare and populate monthly and quarterly finance reports including internal reporting tracker data and summaries • Compile board reporting packs with accurate KPIs, variance analysis, and trend commentary • Support the finance team during month-end and year-end close processes with data extraction and reconciliation

• 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.