Top Remote Artificial Intelligence Data & Analytics Jobs

The Compensation Analytics Manager will lead analyses supporting compensation decisions like hiring and promotions, applying advanced analytics and modeling techniques while collaborating with cross-functional teams.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Lead the Solutions Architecture team to design scalable, customer-centric technical solutions and align them with business needs, while mentoring team members and collaborating across departments.

Lead design and delivery of end-to-end Data & AI solutions on Microsoft Azure. Architect AI/ML applications and scalable data platforms, implement data pipelines, integrate models with business workflows, provide technical leadership on projects, support presales, and promote reusable assets and best practices while staying current on emerging AI and Azure technologies.

Lead architecture and design of scalable Data Lakehouse solutions on GCP, collaborating with clients on data challenges and technical requirements, while ensuring data governance and quality.

The Founding Platform/Infra Engineer will design and manage infrastructure, improve system observability and CI/CD processes, enhance security, and boost developer efficiency at Coverbase.

The Data Scientist will build client-facing RCM dashboards using Power BI, analyze healthcare datasets, and contribute to predictive modeling and project management.

The AML Data Analyst ensures compliance with AML regulations by analyzing financial transactions, conducting risk assessments, and developing detection strategies. Responsibilities include updating procedures, training clients, and enhancing monitoring capabilities.

The Sr. FWA Analyst will identify medical billing errors and fraudulent patterns, support audit processes, and enhance anomaly detection algorithms through data analysis.

As a Staff Product Analytics Engineer, you will build and maintain data infrastructure, collaborate across teams to enhance product data use, and develop tools for data self-service.