Top Remote Artificial Intelligence Data & Analytics Jobs in Seattle, WA

Lead Pager Health’s enterprise data and reporting strategy, build a centralized data and reporting shared services function, own internal dashboards and client-facing analytics, partner with Product and Engineering on data architecture and governance, mentor data teams, prioritize and deliver data initiatives, and drive continuous improvement in tooling, automation, and data storytelling to support growth, efficiency, and client outcomes.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

The Data Platform Engineer II role involves automating data infrastructure, scaling analytics platforms, and ensuring system reliability and security. Key responsibilities include managing CI/CD pipelines, configuring secure networking, and collaborating with Data & Analytics teams to optimize performance.

As a Digital Innovation Co-Op, you'll work on projects involving AI and machine learning to enhance supply chain operations, collaborating with engineers and data scientists to automate workflows and analyze data.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.

Lead statistical analysis for clinical studies in oncology and cardiology, provide project leadership, and mentor junior biostatisticians while ensuring regulatory compliance.

Lead client-facing data solutioning and pre-sales efforts for Consumer verticals, design executable data architectures, support pursuit to close, and provide ~50% hands-on delivery and architectural oversight while ensuring information security and data privacy compliance.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Data Engineer will design, build, and deploy scalable data solutions, optimize data flows, and implement ETL processes, leveraging various cloud platforms and technologies.

The Head of Data Science - Fraud Insights will lead research on fraud analytics, design predictive studies, manage a team, and communicate findings to influence strategy and policy.