Top Machine Learning Jobs in NYC, NY

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

Lead design and deployment of AI solutions, manage end-to-end project delivery, engage clients to define needs, guide teams through complex challenges, innovate processes to improve operations, analyze data trends to inform strategy, and mentor staff while ensuring compliance with model risk management and firm standards.

Design and deliver semantic-data solutions and knowledge graphs to make enterprise information usable for AI and analytics. Build semantic models, data pipelines, integrations with LLMs and retrieval systems, implement validation and CI/CD, support production deployments, mentor junior staff, and contribute to architecture, evaluations, and reusable accelerators.

Lead design and delivery of enterprise semantic-data solutions and knowledge graphs, integrating structured and unstructured data for AI/ML and analytics. Build semantic pipelines, graph-backed services, and integrations (Python/Java), support NLP/embedding use cases, evaluate platforms, define governance/quality controls, run workshops, mentor teams, and drive prototypes through production for regulated clients.

Lead technical design, development, and operational stability of AI/ML solutions on cloud. Drive adoption and governance of enterprise-authorized AI-assisted development, set validation standards, automate recurring remediation, evaluate vendors, and lead engineering communities of practice while coaching teams on responsible, secure AI usage.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Own end-to-end model lifecycle, partner with product/engineering/operations/risk, improve data quality and feedback loops, monitor models in production, and optimize workflows to reduce agent effort and improve consistency.

Design, build, and scale production-grade NLP and LLM-based solutions (search, summarization, classification, recommendations). Drive end-to-end model lifecycle: data quality, evaluation, monitoring, and stakeholder collaboration to improve operational workflows and reduce agent effort.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead execution of a firmwide data product approach: define, develop, register, and govern reusable data products. Coordinate with business and CDAO teams, set KPIs/OKRs, produce strategic reports, advise data leaders, and drive adoption of best practices to enable analytics and AI use across the firm.

Lead product strategy and roadmap for sales-focused applied AI products. Define user journeys and requirements, drive adoption, ensure compliance and data quality, partner with Data Science/Technology to deliver GenAI insights and agentic AI workflows that automate sales processes.

Join the Credit Portfolio Group as a Quantitative Research Associate to build and deploy AI/LLM and ML-driven models, analytics, and automation for portfolio management, trading, and risk. Responsibilities include designing generative AI agents, implementing portfolio optimization and market-making enhancements, automating reporting, collaborating on model approvals, and integrating tools into risk and P&L systems.

Lead end-to-end product strategy and delivery for Business Banking AI/ML products. Drive discovery, prioritize high-value AI use cases, maintain product backlog, define success metrics, ensure risk/governance compliance, and align cross-functional teams to launch scalable, resilient, customer-focused solutions.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.

Lead evaluation strategy for Datadog Dashboards' AI-native system: define metrics, build eval datasets and regression harnesses, measure retrieval and tool-selection, reduce cost/latency, and provide technical leadership and cross-team mentorship to ensure production-quality agent behavior at scale.

The AI Researcher will conduct advanced research in Generative AI and Machine Learning, develop specialized models for observability and code repair, and collaborate with product teams to integrate findings into Datadog's offerings.

The AI Engineer will focus on building AI experiences, deploying models, and enhancing data workflows, working on greenfield applications. Key responsibilities include feature development, architecture collaboration, and shipping impactful product experiences.

The Director of AI Search leads New York Life's AI Search strategy, overseeing AI acquisition strategies and driving AI innovation in marketing experiences. The role requires collaboration across functions to optimize content for AI discoverability and define future-state AI experiences while maintaining compliance and stakeholder alignment.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

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