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

Own the end-to-end data science lifecycle for workforce identity and hiring fraud detection. Analyze identity, device, behavioral, resume, and application data; develop fraud rules, machine learning models, and anomaly detection systems; and support deployment and monitoring in RiskOS. Collaborate on GenAI resume verification and explanation tools, evaluate LLM components, partner with engineering on productionization, and communicate model performance and customer impact to product, GTM, and consulting stakeholders.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Build media intelligence solutions across targeting, planning, measurement, and optimization using statistical analysis, data science, AI, and interactive applications. Partner with Sales and customers to discover needs, prototype solutions, develop dashboards, apply rigorous modeling, incorporate agentic workflows, monitor market trends, and scale validated solutions across the organization. Present complex statistical insights to technical and non-technical stakeholders.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

Build data models, governed metrics, experiments, causal analyses, predictive models, and AI evaluation systems. Connect product and business data to measure enterprise customer value, develop internal AI tools, investigate metric changes, and provide strategic recommendations. The role partners with engineering, mentors analysts and data scientists, and supports analytics across product, sales, marketing, and finance in a hybrid environment.

Lead hands-on delivery of agentic conversational AI solutions for banking IVR systems using Amazon Connect and Amazon Lex. Conduct experimentation, interaction-data analysis, model evaluation, intent and slot tuning, prompt development, and NLU performance optimization. Support LLM and GenAI capabilities, identify risks, and communicate findings and recommendations to leadership and project stakeholders.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Own end-to-end product-led growth analytics, including self-serve funnel measurement, predictive modeling, experimentation, causal inference, data pipelines, and metric governance. Analyze activation, retention, monetization, churn, and usage drivers, then provide strategic recommendations to the CEO and leadership. Build and deploy production data models and machine-learning solutions while partnering across Product, Engineering, Growth, Marketing, and GTM.

Founding Data Scientist responsible for developing product analytics, earned media value metrics, attribution models, forecasting algorithms, and proprietary social commerce measurements. The role partners with engineering and product teams to improve data foundations, translate models into production code, and support public-facing data reports. Requires advanced Python, expert SQL, production software engineering skills, quantitative education, strong communication, and 4–7 years of data science experience.

Lead customer-facing data science for major advertisers, advising on campaign structure, measurement, bidding, and optimization. Translate advertiser insights into product roadmap priorities, define activation frameworks, design experiments, evaluate product features, and establish scalable global playbooks. Partner with product, engineering, sales, and go-to-market teams across regions while communicating complex analysis to technical and executive stakeholders.