Top Remote Artificial Intelligence Data & Analytics Jobs in New York City, NY

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.

The role involves consulting on data-driven healthcare solutions, collaborating with teams, designing demos, and developing business strategies targeted at C-level executives in healthcare.

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 Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

The Solutions Architect leads client integration of Cedar's platform, ensuring successful project delivery through client communication and technical expertise while managing complex data structures and operational workflows.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The Senior Data Center Administrator is responsible for data center operations including inventory management, logistics, vendor coordination, and capacity planning. The role requires technical expertise and strong communication skills to ensure efficient data center environments across multiple locations.

As a Senior Data Engineer at Orita, you'll design and build scalable data pipelines, unify data processes, and enhance our data infrastructure to support machine learning models, ensuring data quality and security throughout the lifecycle.