Top Machine Learning Jobs in NYC, NY

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead analytics, AI, and machine learning strategy and delivery across Payments, including scalable data products, production deployment, governance, model lifecycle management, and Generative AI initiatives. Partner with business, Quantitative Research, Technology, Product, and enterprise AI teams to optimize deposits, liquidity, balance sheets, and revenue. Drive self-service analytics, AI adoption, stakeholder alignment, regulatory compliance, and measurable business outcomes.

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

Designs, prototypes, and scales secure agentic AI systems across enterprise Employee Platforms. The role architects custom agents, orchestration, memory, tool use, evaluation, and human-in-the-loop controls; implements AI solutions in public cloud environments; develops production code; establishes reusable architecture and governance patterns; and advises engineering, product, security, and platform teams on resilient, auditable AI-enabled solutions.

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.

Summer Associate will develop quantitative models and algorithms to support algorithmic trading, pricing, hedging, and portfolio optimization; collaborate with traders, technologists, and risk managers; conduct alpha research and deploy data-driven trading strategies.

Summer analyst will build and maintain quantitative models and algorithms for systematic trading and market-making, value and hedge transactions, conduct alpha research, calibrate models, optimize portfolios, and collaborate with traders, technologists, risk, and sales to deploy data-driven trading strategies.

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 Confido’s data science function for Revenue Growth Management, developing pricing, promotion, forecasting, causal inference, and optimization models. Translate predictions into actionable recommendations under budget, margin, and retailer constraints. Partner with AI/ML engineers on productionization, evaluation, backtesting, and monitoring. Hire and mentor a team of data scientists while setting technical direction with company leadership.

Build enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, tool-calling frameworks, and machine learning. Partner with data scientists, ML engineers, and product managers to develop, evaluate, validate, fine-tune, and deploy AI pipelines over large numeric and textual datasets. Translate complex technical work into business outcomes while researching emerging AI methods.

Leads a US Card data science team developing machine learning solutions for customer behavior prediction and valuation forecasting. Partners with data scientists, engineers, product managers, and business stakeholders; builds models through design, training, validation, evaluation, and implementation; analyzes large-scale numeric and textual data; translates technical findings into business outcomes; and develops team talent.

Develop enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, and tool-calling frameworks. Partner with data scientists, machine learning engineers, and product managers to build, evaluate, validate, fine-tune, and deploy AI pipelines across numeric and textual data. Apply statistical modeling, machine learning, deep learning, NLP, information retrieval, and cloud technologies while translating complex technical work into business outcomes.

Build and deploy production-grade AI and machine learning solutions within regulated customer environments. Develop specialized AI workflows, model deployment and monitoring systems, cloud and data integrations, applications, CI/CD extensions, and secure platform solutions. Advise data science teams, lead customer engagements, troubleshoot infrastructure, create reusable delivery materials, and provide product feedback to engineering, support, and product teams.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.

Lead data science and NLP initiatives for AI-powered products, partnering with engineers, machine learning specialists, and product managers. Build, fine-tune, evaluate, validate, and operationalize large language and machine learning models at scale. Apply advanced NLP, deep learning, and model training techniques while supporting model risk compliance. Translate technical findings into business outcomes and develop team talent.

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.

Leads AI engineering strategy and execution across Mastercard, overseeing AI/ML architecture, deployment, monitoring, optimization, governance, and risk management. Partners with product, technology, data science, and business leaders to deliver scalable AI solutions and measurable business impact. Provides technical mentorship, establishes engineering standards, evaluates emerging technologies, and guides enterprise transformation through generative AI and machine learning.

Develop and deploy advanced machine learning and data science solutions across large numeric and textual datasets. Partner with data scientists, engineers, and product managers to build customer-focused products. Lead model design, training, evaluation, validation, and implementation while researching emerging technologies and translating complex analyses into business outcomes.

Develop and implement machine learning models across the full lifecycle, from design and training through validation and deployment. Analyze large numeric and text datasets using Python, cloud computing, and big data technologies. Partner with data scientists, engineers, and product managers to deliver customer-focused products, translate complex analyses into business outcomes, and research emerging methods. Apply statistical modeling, classification, clustering, sentiment analysis, time series, and deep learning techniques.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Develop predictive models and machine-learning solutions for cybersecurity, payment fraud detection, and vulnerability analytics. Analyze large datasets using distributed processing, identify patterns, create technical rules, integrate cybersecurity and transaction data, and design performance metrics. Collaborate with product, engineering, and operations teams, translate stakeholder needs into analytical solutions, and communicate insights to leadership. The role also involves graph databases and graph machine learning, including graph neural networks.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production systems involving NLP, document AI, OCR, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices. Partner with senior stakeholders, define roadmaps and success metrics, improve engineering standards, conduct technical reviews, and mentor engineers.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Conduct applied AI and machine learning research spanning multimodal intelligence, recommendation, ranking, prediction, foundation models, agents, and decision-making. Define research problems, evaluate and develop models using proprietary data, run rigorous experiments, create training signals and evaluations, and transition successful approaches into production systems. Collaborate with engineering and product teams while shaping the company’s research agenda and technical standards.