Applied Artificial Intelligence​/Machine Learning Lead - Vice President

As an Applied AI/ML Vice President within Global Private Bank, you'll own the full lifecycle of high-impact models serving clients across wealth management, lending, and advisory, from problem framing with business stakeholders to production deployment 'll work on some of the most data-rich, complex client problems in financial services, with the infrastructure and resources of one of the world's largest institutions behind you.

We're building AI-native capabilities at the intersection of cutting-edge research and real-world impact.

Job Responsibilities:

• Define and scope AI/ML problem statements in partnership with Private Bank business leads, translating ambiguous client or operational pain points into tractable modeling problems
• Design, build, and deploy end-to-end ML solutions — including generative AI, NLP, and classical machine learning— across client service, risk, and operational efficiency use cases
• Own model quality, evaluation frameworks, monitoring, drift detection, and iteration post-deployment
• Drive productionization and MLOps practices in collaboration with engineering, working across distributed data infrastructure
• Stay current on applied research; evaluate and adapt emerging techniques — new architectures, agentic frameworks, multimodal models — for relevance to the Private Bank's problem space and translate promising work into production-ready solutions
• Mentor junior data scientists and help set technical standards for the team
• Collaborate across JPMorgan
  
  Chase's broader AI/ML community, model risk, compliance, and peer LOBs to align on standards, share learnings, and amplify the team's impact firm-wide

Required qualifications, capabilities, and skills:

• Master's or PhD in Computer Science, Statistics, Applied Math, Data Science, or related quantitative field
• Atleast 5 years of hands‑on ML experience in production environments.
• Deep expertise in NLP, including modern LLM fine‑tuning, RAG pipelines, prompt engineering and the design and deployment of multi‑step AI agents
• Strong Python skills; proficiency with PyTorch, Tensor Flow, Scikit‑learn and other libraries
• Experience with large‑scale data processing:
  Spark, Hive, SQL
• Proven ability to communicate technical work to non‑technical stakeholders

Preferred qualifications, capabilities, and skills:

• Financial services experience