Vice President — Principal Applied AI Data Scientist

JPMorgan Chases Asset & Wealth Management Finance organization is advancing the frontier of agentic AI deploying digital workers that transform forecasting analytics and decision support. As Vice President you will lead the design deployment and scaling of large language model (LLM) agents and AI-driven solutions driving innovation and business impact across finance workflows. You will shape strategy architect robust systems and mentor teams to deliver trusted actionable insights for complex financial questions.

Job responsibilities

• Define and execute the roadmap for agentic AI and digital worker solutions in Finance aligning with business priorities and emerging technologies.
• Identify and prioritize high-value use cases translating ambiguous business challenges into measurable outcomes
• Lead cross-functional teams collaborating with Finance Product Engineering and Operations to deliver scalable production-grade AI solutions.
• Architect build and scale LLM agents for finance workflows using advanced techniques such as LangGraph retrieval augmented generation (RAG) multi-agent orchestration tool use and multi-step reasoning
• Oversee the development of robust data and inference pipelines in Python and SQL; integrate agents with APIs microservices BI/reporting tools and cloud platforms (AWS Azure GCP)
• Leverage vector databases embeddings and distributed compute frameworks (e.g. Databricks Snowflake PySpark) for efficient retrieval and performance optimization.
• Drive research and development initiatives exploring Gen AI Agentic AI and autonomous workflow patterns.
• Mentor and upskill teams; deliver enablement materials documentation and best practices for AI adoption.
• Foster a culture of innovation experimentation and continuous improvement.
• Translate model outputs into user-friendly insights and analytics for end users enabling data-driven decision making.
• Communicate complex technical concepts to senior stakeholders; deliver compelling data visualizations and narratives.

Required qualifications capabilities and skills

• 8 years in data/ML roles including 4 years building and operating production ML applications; handson experience with LLMs.
• Strong Python and SQL;
• Practical knowledge of RAG prompt engineering finetuning function/tool calling and vector stores.
• Experience with cloud platforms (e.g. AWS Azure or GCP) and modern data stacks (e.g. Databricks or Snowflake).
• Familiarity with LLM frameworks and orchestration (e.g. LangChain or LlamaIndex) and REST/GraphQL API design.
• Proficiency in analytics and applied statistics; ability to design experiments and evaluate business impact.
• Excellent communication and stakeholder management; comfort working across Finance Technology and Operations.

Preferred qualifications capabilities and skills

• Experience building multiagent systems autonomous workflows or task planners.
• Knowledge of model safety bias and privacy techniques; experience with model risk management and governance.
• Exposure to observability tools (logging tracing telemetry) and A/B testing.
• Background integrating agents with BI/reporting and workflow tools; familiarity with Tableau or similar is a plus.
• Experience with GPUs/accelerators containerization and infrastructureascode.
• Experience with PySpark or distributed compute.

What success looks like

• 90 days: deliver a pilot finance agent with RAG and evaluation metrics integrated with key data sources and APIs.
• 6 months: scale agents across multiple workflows establish guardrails and monitoring and demonstrate clear improvements in cycle time accuracy or user satisfaction.