• Application of theoretical methodological knowledge in a practice-oriented environment. • Analysis of objective smartphone usage data and subjective data from a large sample. • Descriptive and analytical examination of the dataset.

• Organize and analyze data across a variety of formats to support Pearl’s provider submission process, inclusive of ACO Operations team workflows. • Build and maintain structured datasets and tracking tools to support team reporting, leveraging AI tools. • Collaborate cross-functionally to address provider submission and workflow gaps. • Adapt quickly to shifting priorities, bringing exceptional attention to detail and a proactive, solutions-oriented approach to every task.

• Own client-facing analytics: build, maintain, and continuously improve dashboards and reports. • Partner closely with Customer Success to understand client needs and deliver insights. • Design and build production-quality LookML models, explores, and dashboards in Looker. • Write complex SQL queries across Redshift, Aurora/MySQL, and Athena. • Develop Python scripts and notebooks for data wrangling and automated reporting. • Leverage AI tools in day-to-day analytics work. • Identify and champion AI-powered use cases within the analytics workflow. • Proactively explore data to surface trends, anomalies, and present findings.

• Partner with Product Managers to define KPIs, measure success, and support continuous discovery practices • Analyze product usage, customer behavior, and funnel performance to identify trends and opportunities • Support experimentation and A/B testing efforts by measuring results and translating findings into actionable recommendations • Build and maintain dashboards and reporting in tools such as Metabase and Amplitude • Develop analyses that help teams improve product adoption, engagement, and retention • Work with cross-functional teams to ensure consistent metric definitions and trusted reporting • Leverage SQL, dbt, and analytics tools to transform and analyze product data • Enable self-service analytics by training stakeholders on dashboards, reporting tools, and best practices • Communicate complex findings clearly and effectively to both technical and non-technical audiences • Help foster a culture of experimentation, curiosity, and data-driven decision-making across Product and Engineering teams