Top AI & Machine Learning Jobs in New York City, NY

As a Senior Data Scientist at Capital One, you will lead AI-powered product development using machine learning technologies, focusing on NLP and large language models to enhance customer experiences.

Evaluate and integrate alternative (non-traditional) data into credit decisioning: engineer features, build and validate machine learning models, develop scalable processing and monitoring systems, guide dataset applications and vendor risk governance, and collaborate cross-functionally to implement data-driven solutions.

Lead end-to-end strategy and execution to integrate Google generative AI products across Capital One, drive rapid experimentation, partner with Google product leadership, build quantifiable value cases, balance user experience with enterprise controls, and lead cross-functional teams to scale AI-driven productivity and automation.

Lead ML engineer responsible for productionizing ML systems: design, build, deploy, monitor, and scale models and data pipelines on cloud (AWS). Collaborate cross-functionally, apply MLOps best practices (CI/CD, testing, governance), and maintain high availability and explainable AI standards.

Lead design, build, and deploy production-scale generative AI and agentic systems: foundation model training, LLM inference, vector search, guardrails, evaluation, observability, and performance optimization across cloud infrastructure.

Lead bureau data strategy by building end-to-end machine learning models and extracting insights from large credit bureau datasets. Partner with engineers and product managers, use Python, cloud, and big-data tools, translate technical work into business outcomes, and develop talent.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Drive intake, prioritization, and refinement of early-stage GenAI use cases for Card products. Break down epics into features and user stories, define acceptance criteria and MVPs, prioritize roadmaps using data, and collaborate with designers, engineers, data scientists, and stakeholders to deliver customer-focused AI-driven product experiences in an agile environment.

Lead GenAI transformation for Field Sales within Business Cards & Payments. Design a horizontal foundation to centralize trusted context for AI apps, enable GenAI experiences across the sales funnel, and own feedback-loop strategy to capture, evaluate, and feed AI outputs for continuous model improvement and production measurement at scale.

Lead onboarding and roadmap for integrating conversational AI into an enterprise QA platform. Design automated AI QA, evaluation pipelines, and closed-loop feedback systems to scale consistent, accurate assessments across complex customer intents.