Top Machine Learning Jobs in NYC, NY

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

The Staff Applied Scientist will lead the evaluation strategy for AI agent integrations at Datadog, defining metrics and building datasets for quality improvements in agent performance through applied research and technical leadership.

Lead a team in developing AI and ML models focusing on detection and text intelligence. Collaborate with product managers and support team development for impactful AI features.

Develop applied AI, machine learning, optimization, and statistical models to solve complex business problems for Chase Finance and Business Management. Analyze large datasets, identify actionable insights, contribute throughout the modeling lifecycle, and communicate findings to senior stakeholders. The role also involves serving as a subject matter expert, advising partners, and introducing innovative analytical approaches.

Lead independent oversight of operational, technology, and cyber risks for AI and machine learning platforms and use cases. Assess threat exposures, analyze emerging risks and related events, challenge first-line mitigation strategies, evaluate controls, and collaborate with technology, risk, and corporate-function stakeholders to support resilient and responsible AI adoption.

Lead data science and generative AI initiatives for Merchant Services, developing LLM-powered assistants, workflow automations, data pipelines, dashboards, and intelligent analytics solutions. Design prompts, test and monitor AI models, improve reliability, and partner with stakeholders on data models, algorithms, customer insights, and client analytics. Use Python, SQL, Snowflake, Tableau, and related tools to deliver commercially impactful solutions.

Analyze Core customer behavior, engagement, retention, product usage, and economics to identify strategic opportunities. Translate complex analyses into executive-ready insights, presentations, and recommendations. Partner with product, marketing, finance, technology, data science, and governance teams to shape customer growth priorities. Apply AI, statistics, machine learning, SQL, and visualization techniques to solve unstructured business problems while aligning recommendations with business, risk, and compliance standards.

Develops end-to-end data science and generative AI solutions for payments and merchant services. Responsibilities include designing prompts, building chat assistants and workflow automations, creating Python and SQL data pipelines and Tableau dashboards, testing and monitoring AI models, developing algorithms and data models, and delivering customer insights and analytics. The role collaborates with business stakeholders and data teams to improve decision-making, automate processes, and support commercial growth.

Develop predictive models forecasting credit outcomes, apply explainability techniques to complex models, and create AI-driven decision frameworks for regulated credit decisions. Analyze large, diverse datasets and apply statistical modeling, machine learning, deep learning, classification, clustering, sentiment analysis, and time-series methods. Collaborate with machine learning engineers, product managers, credit analysts, and data engineers to deliver scalable solutions.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

11-week quantitative research internship developing and implementing mathematical and trading models. Responsibilities include model conceptualization, backtesting, live implementation, using unconventional data, and conducting statistical research to refine trading signals and monetization systems.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Leads AI technology integration and deployment within the tax practice. Oversees data infrastructure, pipelines, modeling, quality validation, and multi-source integration. Develops Python-based analytics solutions using machine learning and natural language processing, conducts rapid experimentation, and drives data-informed innovation. Coaches and develops high-performing teams while collaborating with stakeholders and advising on strategic initiatives.

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Leads Pfizer’s enterprise applied AI strategy, architecture, governance, operating model, and transformation efforts. Drives portfolio-level technical decisions, reusable capabilities, standards, and measurable business outcomes. Provides hands-on architecture and code review using modern AI and agent frameworks while leading engineers, contractors, and vendors. Oversees quality, security, performance, spending, and technical direction, mentors technical leaders, and evaluates emerging technologies and industry trends.

Lead data strategy for large 2D and 3D media datasets used in production machine learning. Coordinate with ML, annotation engineering, and technical program management teams; translate project goals into actionable data specifications; oversee dataset curation, filtering, and processing; and ensure data integrity, representativeness, and quality through analysis and inspection.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Leads the Partnerships Customer Management Data Science team, mentoring specialists and owning the strategic roadmap for predictive modeling across credit risk, account usage, and portfolio financials. Oversees end-to-end machine learning model development, validation, and production implementation. Partners with analysts, engineers, and product managers while applying Python, AWS, Spark, H2O, and other technologies to analyze large-scale data and deliver customer-focused business outcomes.

Lead design and delivery of AI and GenAI data solutions for Tax, including analytical modeling, prompt engineering, ML/NLP, Azure AI Services, LangChain, and RAG. Manage and coach teams, validate outcomes with clients, and translate raw data into actionable insights to drive business growth.