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

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Summer intern assisting with IT solution design and implementation, supporting cybersecurity initiatives through security protocol analysis, and collaborating with data science teams to analyze data and generate actionable insights. The role offers hands-on experience, mentorship, and professional development within Honeywell Aerospace. Candidates must remain enrolled in a relevant degree program, maintain at least a 3.0 GPA, and meet U.S. person requirements.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

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

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Leads data science for Anthropic’s Core Apps product organization, partnering with product leaders on strategy, roadmaps, investment decisions, and success metrics. Manages and develops a team of Product Data Scientists, establishes experimentation and causal inference standards, leads high-impact analyses, communicates recommendations to executives, and builds foundational analytics practices and infrastructure. Requires extensive product data science experience, people management, strong statistical expertise, and fluency in Python and SQL.

Own the modeling work behind June’s AI interview evaluation engine. Build, evaluate, and deploy models that score and summarize interviews, develop offline and online evaluation infrastructure, and monitor calibration, drift, and bias. Partner with engineering and product to integrate models into the product and improve them through real-world usage. Help establish data-team tooling and standards while working with LLMs, NLP, ambiguous data, and fairness considerations.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, experiments, and recommendations that drive decisions and measurable outcomes. Partner with cross-functional stakeholders, communicate insights clearly, and independently solve ambiguous problems using SQL and Python.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, first-principles problem solving, independent data interpretation, persuasive communication, and ownership of ambiguous business problems.