Applied AI ML Executive Director, Chief Data & Analytics Office

JPMorgan Chase & Co.•Jersey City, NJ

2d•$223,000 - $325,000

About The Position

Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office. As a leader in applied AI and machine learning, you’ll have the opportunity to work on high-impact projects that influence the way we do business across multiple domains. Collaborate with talented colleagues, leverage cutting-edge technologies, and see your work make a tangible difference. We value curiosity, technical excellence, and a passion for solving complex problems. If you’re ready to accelerate your career and drive meaningful change, we want to hear from you. As an Applied AI ML Executive Director in the Chief Data and Analytics Office, you will lead the design, build, and scale of Generative AI and agentic AI capabilities that solve complex operational challenges. You will guide a team that delivers production systems—spanning model development, software engineering, deployment, and continuous improvement—while building reusable services that accelerate adoption across teams. You will partner closely with senior stakeholders to prioritize use cases, measure impact, and drive enterprise-scale transformation.

Requirements

PhD in Computer Science or a related quantitative discipline with 8+ years of relevant experience, or MS in Computer Science (or related field) with 12+ years of relevant experience
Formal training or certification in applied AI and machine learning concepts
Proven track record of deploying AI/ML applications into production environments at scale, including reliability, monitoring, and lifecycle management
Strong understanding of AI/ML fundamentals, including experimental design, evaluation methods, and data analysis techniques
Experience with distributed computing patterns for model training, model serving, and state persistence in production systems
Demonstrated ability to design systems that incorporate user feedback loops to refine agent behavior and improve performance over time
Demonstrated experience building, mentoring, and leading high-performing AI/ML teams delivering complex outcomes with cross-functional partners
Strong communication and stakeholder management skills, including the ability to influence prioritization and align delivery to business value

Nice To Haves

Experience deploying and operating models on Amazon Web Services platforms, including Amazon SageMaker and/or Amazon Bedrock
Experience building agentic or multi-agent systems, including orchestration patterns, tool-use design, and guardrails for safe and reliable execution
Experience establishing evaluation strategies for Generative AI systems (for example, quality scoring, test sets, and human-in-the-loop review)
Experience building reusable AI/ML platforms or shared services adopted by multiple teams across an enterprise
Familiarity with modern MLOps and LLMOps practices, including automated deployment, monitoring, and continuous improvement workflows

Responsibilities

Architect end-to-end Generative AI and agentic AI solutions that automate complex operational workflows with measurable business outcomes
Lead the design and delivery of multi-agent systems that decompose complex problems, orchestrate tasks, and reliably execute end-to-end workflows at scale
Translate business objectives into robust AI/ML product and platform capabilities, balancing speed of delivery with reliability, security, and long-term maintainability
Build reusable frameworks, libraries, and services that enable other AI teams to standardize patterns for model development, evaluation, deployment, and monitoring
Establish production engineering rigor across AI/ML delivery, including observability, performance tuning, incident readiness, and operational runbooks
Partner with cross-functional stakeholders to identify high-value opportunities, define success metrics, and scale solutions through adoption and change management
Mentor and develop a high-performing team of AI engineers and researchers, creating a culture of technical excellence, experimentation, and continuous learning
Drive governance for experimentation and iteration, ensuring feedback loops and evaluation practices improve model and agent behavior over time