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

The Revenue Analytics Manager will analyze customer success and partner analytics, delivering insights to drive retention and revenue growth, working cross functionally with leadership and data teams.

The Associate Applied Data Scientist will ensure data accuracy, clean complex datasets, build machine learning models, and collaborate cross-functionally to drive data strategy.

As a founding Developer Advocate, you'll build developer infrastructure, drive growth, and champion developer needs by creating engaging content and collaborating cross-functionally.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

The role involves managing data pipelines for AI and quantitative research, focusing on L3 market data architecture and integrity, API development, and collaboration with researchers.

Lead the implementation team for Rillet's ERP platform, managing customer onboarding and optimizing processes while mentoring team members and collaborating with cross-functional partners.

Serve as the primary data partner to fundraising and marketing, building production-grade data models, defining KPIs, creating automated data products, delivering revenue forecasts and donor cohort analyses, and translating insights to drive acquisition, retention, and revenue growth.

The AWS Data Engineer will design, build, and optimize data pipelines on AWS, ensuring performance and data quality for analytics. Responsibilities include developing ETL processes, collaborating with teams, and monitoring data workflows.

As an Analytics Engineer, you will transform raw data into reliable insights, build data models, and conduct analysis to support product and business decisions.

The role involves improving reserving models, developing predictive models using telematics and claims data, and influencing pricing and underwriting decisions through data analysis.