Top Remote Artificial Intelligence Data & Analytics Jobs

The Facets Systems Analyst will perform development and maintenance tasks in Facets, lead a team of developers, and ensure quality outcomes through testing methodologies while maintaining compliance with healthcare standards.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

As a Power Markets Analyst, you'll provide market analytics, insights, and collaborate with engineering and policy teams to optimize performance in established electricity markets.

As a Forward Deployed Data Analyst, you will configure and implement data systems for clients, ensuring their analytics are accurate and actionable. You'll work with teams to refine metrics and improve data processes, ensuring value from day one.

As a Memory Developer, you'll record household tasks, shape the robot's memory, and help train its AI for better performance.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

Design and develop reports and dashboards (Tableau, Power BI, Looker, D3) by partnering with stakeholders. Use SQL to explore and import data, create prototypes, ensure data accuracy through quality checks, optimize BI deliverables, document and train end users, and promote best practices in data visualization and UX.

As a Senior Data Engineer, you will design and maintain scalable data pipelines, focusing on architecture, ingestion, storage, and analytics on GCP. You'll collaborate with stakeholders and optimize data structures for performance while ensuring adherence to data governance and compliance standards.

The Strategic Insights Lead analyzes healthcare data to shape business strategies, delivers impactful insights, and collaborates with various teams to ensure clarity and actionability in findings.

The Sr. FWA Analyst identifies fraud, waste, and abuse in medical billing through data analysis, supporting engineering and data science teams with validation and tuning of detection algorithms.