Top Machine Learning Jobs in NYC, NY

Develop and deploy machine learning, generative AI, and agentic AI solutions across the model lifecycle. Responsibilities include feature engineering, model training and validation, statistical experimentation, cloud operationalization, LLM/RAG implementation, agent evaluation, API and dashboard integration, UI prototyping, monitoring, governance, documentation, and CI/CD collaboration. The role partners with data scientists, engineers, product teams, and business stakeholders to deliver secure, scalable, outcome-driven AI capabilities.

Develop and deploy scalable optimization, simulation, and machine learning models for banking decision systems. Translate complex business challenges into mathematical problems, build production-grade algorithms using Python and optimization solvers, design experiments, and integrate modeling solutions with software systems. Collaborate with Product, Engineering, and Business teams to improve customer experiences, risk policies, and operational decision-making.

Lead end-to-end data science initiatives for economic and financial analysis. Responsibilities include curating complex datasets, developing statistical, machine learning, econometric, and generative AI solutions, validating data sources, identifying economic trends, generating forecasts, and communicating insights to technical and executive audiences. The role partners with economists, strategists, product owners, and data scientists while advancing LLM and GenAI adoption.

Leads the full analytics lifecycle for Spectrum Reach’s advertising sales business, including data preparation, exploratory analysis, predictive modeling, validation, productionization, forecasting, and interpretation. Develops scalable analytics products, synthesizes recommendations, presents findings to diverse audiences, manages project execution, and mentors junior data professionals. Requires advanced statistical, machine learning, programming, cloud, and big-data expertise.

Lead data science and model risk initiatives by partnering with data scientists, engineers, and product managers. Build, evaluate, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical methods, backtesting, and model-risk expertise while translating complex analyses into business outcomes. Responsibilities also include researching emerging technologies, improving decision-making, and developing talent.

Conduct research and develop machine learning solutions involving embeddings, foundation models, transformer models, and customer behavioral analytics. Build, evaluate, validate, and operationalize models using large-scale transaction and textual data. Partner with engineering, product, and business teams to improve customer experiences and outcomes across marketing, servicing, and fraud prevention. Analyze data with Python or Scala, Spark, AWS, and relational databases.

Provides technical leadership for AI/ML engineering teams, designs scalable machine learning platforms and data pipelines, delivers generative AI and NLP products, shapes technology roadmaps and infrastructure investments, and promotes innovation across enterprise AI practices.

Leads the technical strategy and production delivery of agentic AI and machine learning products for small business lending. Responsibilities include designing prompts, retrieval, tools, evaluation, routing, memory, safety controls, and observability; defining outcome measurements; partnering with Product and Engineering; managing delivery risks and dependencies; mentoring team members; and communicating recommendations to senior stakeholders in a regulated financial environment.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Conduct quantitative research for macro and fixed-income markets by developing alpha-generating investment models. Responsibilities include idea generation, feature development, alpha forecasting, monetization, portfolio construction, portfolio management, dataset analysis, and applying artificial intelligence and machine learning techniques. The role involves collaborating with quantitative researchers and developers to solve complex investment problems and communicate findings clearly.

Develop sophisticated quantitative trading models using scientific methods, machine learning, statistical techniques, and large datasets. The researcher will create and test investment ideas, collaborate with engineers, follow current academic research, attend seminars and conferences, and share insights in statistics, machine learning, and data science.

Conduct quantitative research for investment strategies using advanced statistical and mathematical modeling, machine learning, data analysis, portfolio optimization, and algorithm development. Build predictive models and analyze price formation, risk, and financial market drivers. Collaborate with researchers, engineers, and traders to develop scalable systems and solve real-world investment problems.

Leads data science and model risk initiatives, partnering with data scientists, engineers, and product managers. Builds, evaluates, validates, and implements machine learning models using large-scale data and technologies including Python, AWS, H2O, and Spark. Translates complex statistical findings into business outcomes, researches emerging methods, validates and backtests models, and develops talent. Requires quantitative education, substantial analytics experience, machine learning expertise, and relational database experience.

Leads the design, development, and operation of enterprise AI platforms across public and private clouds. Responsibilities include MLOps, model deployment and inference, infrastructure automation, observability, monitoring, governance, reliability, incident response, and platform scalability. The role provides technical leadership, mentors engineers, influences architecture, supports AI adoption, and partners with security, infrastructure, risk, and governance teams to deliver secure and resilient machine learning and generative AI capabilities.

Develop AI/ML analytics, automation, and decision-making tools for Securities Services, Payments, and CIB Treasury. Responsibilities include designing scalable transaction-data architecture, optimizing deposit management and net interest income under financial constraints, developing models from prototype through production, presenting quantitative insights, and collaborating across quantitative research, treasury, technology, investment, and data analytics teams.

Leads a multidisciplinary data science and data engineering team supporting Finance. Sets strategy, prioritizes initiatives, guides analytical models and data architecture, advances AI and machine learning applications, improves data quality and reliability, establishes scalable operating processes, and communicates technical recommendations to Finance and executive stakeholders. The role also manages talent, oversees forecasting and decision-support outcomes, and drives measurable business impact.

Leads GitLab’s Product, Sales, and Finance Data Science teams, managing managers and a 12-person organization. Sets data science standards, prioritizes investments, partners with executives, drives product adoption and revenue growth, and oversees experimentation, causal inference, predictive modeling, and AI initiatives. Develops talent, builds succession, and collaborates with Analytics Engineering and Data Engineering on a shared data and AI roadmap.

Owns the full lifecycle of enterprise AI and agentic products, from identifying business use cases and defining requirements through deployment, evaluation, optimization, and adoption. Partners with engineering, product, program, and business teams to build LLM, RAG, and multi-agent solutions, establish quality frameworks, analyze performance metrics, and deliver scalable AI-driven workflows and analytics capabilities.

Build and productionize AI agents for healthcare claims review. Responsibilities include agent orchestration, LLM fine-tuning, RAG pipelines, reinforcement learning alignment, evaluation frameworks, production integration, and HIPAA-compliant safety guardrails. The role requires Python, PyTorch, Hugging Face, production RAG experience, AI agent development, and applied reinforcement learning expertise.

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Leads data science for Capital One’s US Card business, developing and deploying machine learning models across marketing, acquisitions, underwriting, and fraud prevention. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using cloud and open-source technologies; translates complex insights into business outcomes; and develops talent while guiding technical innovation.

Leads the Bureau Data Strategy team in extracting insights from credit bureau data and shaping underwriting data usage. Partners with data scientists, engineers, and product managers; develops machine learning models from design through implementation; analyzes large-scale numeric and textual datasets; translates technical findings into business outcomes; and develops team talent.

Lead data science for Capital One’s US Card organization by building and deploying machine learning models across the credit card lifecycle. Partner with software engineers and product managers, analyze large-scale customer data using Python, cloud, and distributed computing technologies, and translate statistical insights into business outcomes. The role also emphasizes innovation, stakeholder communication, technical leadership, talent development, and managing complex data science initiatives.

Leads the analytics and artificial intelligence strategy for Auto Commercial Solutions, overseeing data scientists and quantitative analysts. The role develops machine learning, predictive analytics, generative AI, experimentation, governance, and scalable data products to improve dealer acquisition, relationship growth, sales effectiveness, portfolio performance, and commercial lending opportunities. It partners with senior leaders across product, technology, risk, finance, and sales while translating complex insights into business recommendations and building a high-performing analytics organization.

Build AI agent orchestration, routing, planning, memory, and action systems. Develop and deploy machine learning models for search ranking and proactive suggestions, integrate modern LLMs, and design evaluations to improve quality, speed, and cost. The role requires independent ownership, strong product sense, and collaboration in a fast-paced environment.