Top Healthtech Data & Analytics Jobs in Seattle, WA

Leads analytics for Chewy’s Voice of Customer organization by integrating customer, operational, survey, sentiment, and behavioral data. Builds dashboards, measurement frameworks, alerts, governance standards, and self-service analytics. Identifies customer friction, risks, root causes, and business impacts; conducts forecasting, experimentation, segmentation, and regression analyses. Develops executive-ready recommendations, partners with senior stakeholders, monitors customer-impacting events, and coaches analysts and cross-functional teams.

Lead end-to-end quantitative market research, including research planning, surveys, advanced statistical analysis, synthesis, and stakeholder presentations. Build and manage brand and sentiment measurement programs, identify customer and market trends, and provide strategic recommendations for growth and product opportunities. Partner with business leaders, promote research-driven decision-making, and mentor junior researchers while scaling the organization’s research practice.

Design and execute advanced statistical and data analyses for client projects, using tools such as R, Tableau, SAS, VBA, and Excel. Translate data into actionable insights, communicate findings to clients and internal teams, collaborate on solution implementation, and take ownership of project deliverables.

Own implementation, maintenance, and optimization of client eligibility and claims data feeds. Analyze feed performance, troubleshoot discrepancies, produce error reports, test integrations with external partners, and improve internal tools, documentation, and processes to increase automation and reliability.

Own actuarial models and client-facing reporting measuring cost savings, outcomes, and performance of healthcare prior authorization programs. Analyze authorization and claims data, conduct impact evaluations, reconcile projected and realized results, investigate trends, and present executive-level insights to customers and leadership. Partner with clinical, customer success, data, and actuarial teams while maintaining reproducible, auditable methodology and documentation.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Analyze healthcare claims and clinical data to build data models, analytical pipelines, predictive and risk-model inputs, dashboards, scorecards, and reusable analytics assets. Translate ambiguous customer and clinical questions into actionable data plans, manage delivery across stakeholders, present findings to customers, maintain quality standards, mentor junior analysts, and contribute to analytics roadmaps, automation, and scalable methodologies.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.