What are the responsibilities and job description for the Applied Artificial Intelligence/ Machine Learning Lead - Vice President position at JPMorgan Chase?
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 at scale. You'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 JPMorganChase'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, TensorFlow, 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