Top Senior Level Data & Analytics Jobs

The Revenue Cycle Manager oversees medical billing processes, ensuring accurate coding and efficient revenue management. They implement billing system configurations, manage revenue cycle reports, and liaise with clients and the billing department. The role requires supervisory capabilities and expertise in handling payer interactions and audit assistance.

The Data Science Developer analyzes large datasets, develops machine learning algorithms, collaborates with medical professionals and software engineers, designs software tools for medical image processing, implements deep learning architectures, and ensures compliance with regulatory standards.

The Staff Optimization Scientist will optimize multi-agent robotic systems by developing fulfillment algorithms in collaboration with engineering and operations teams, focusing on resource allocation, routing, and efficiency improvements. The role requires expertise in optimization and statistical software, with responsibilities including building prototypes and optimizing system operations using data-driven strategies.

This is an invitation to join a talent community for Senior Operations Leaders at HelloFresh. Members receive updates on job openings, career tips, and events.

As a Senior Data Scientist, you'll drive the technical roadmap for risk monitoring, develop prediction models, analyze data, and collaborate across teams to deliver data-driven insights. You will also manage and mentor team members while ensuring high-performance models through excellent data science practices.

The Data Scientist will collaborate with Go-to-Market teams, develop data models, perform impact analysis, and leverage data to optimize business initiatives at Figma. They will be responsible for data integrity and driving insights into strategic decision-making processes across teams.

As the AI Data Architect/Data Lead at Golin, you will develop and implement the agency's long-term data strategy to enhance AI-driven solutions. This includes creating a data lake, ensuring compliance with data governance, acquiring partnerships for data, and driving innovative data-driven capabilities while collaborating with various stakeholders.