Top Artificial Intelligence Data Science Jobs in New York City, NY

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

The Senior Manager, Data Science & Analytics will lead the analytics strategy for client engagement, focusing on data-driven insights and marketing performance to assist in customer acquisition efforts.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

The role involves developing graph algorithms, data pipelines for identity verification, and supporting data analysis for compliance products.

As a Senior Data Scientist, you will design and deploy machine learning and graph algorithms to enhance identity trust solutions, own end-to-end projects, and collaborate with product and engineering teams to improve KYC and compliance products.

Own end-to-end scientific data acquisition programs for AI-lab customers: scope projects, recruit and manage experts, design protocols, define quality and metrics, run pilots to production, and coordinate cross-functional teams to deliver model-useful datasets.

Embed with a core business unit to define problems, build metrics, run analyses and experiments, surface insights, and drive data-backed recommendations end-to-end to improve product, growth, AI/automation, or partnerships.

Embedded on a core business team, you will define analytical problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed recommendations end-to-end to improve product, growth, AI, or partnerships.

Build and evolve AI systems to enhance investment insights, integrating models and optimizing outputs for professional investors in a fast-paced environment.

The Data Scientist will analyze data to enhance business performance, engage customers, and optimize product strategies through experimentation and metrics establishment.

Lead experimentation and causal inference efforts: design, run, and analyze A/B tests; establish methodological standards; support cross-functional analytics escalations; produce research and benchmarks; enable predictive and segmentation modeling; and deliver fast, actionable analytics for product, marketing, and operations stakeholders.