Vice President — Principal Applied AI Data Scientist

Testing Site - JPMC; Jersey City (NJ); On-site; USD 180,000 - 280,000; Full time

Job summary

JPMorgan Chase’s Asset & Wealth Management Finance division is pursuing agentic AI to transform forecasting, analytics, and decision support. You will design, deploy, and scale LLM agents and AI-driven solutions across finance workflows, shaping strategy and mentoring teams to deliver trusted insights.

As Vice President, you will oversee data/inference pipelines, cloud integrations, and multi‑agent orchestration, partnering with Finance, Product, Engineering, and Operations to achieve measurable

Qualifications

• 8+ years in data/ML roles, including 4+ years building and operating production ML applications.
• Hands-on experience with large language models (LLMs) and AI-driven solutions.
• Strong Python and SQL skills; proficient in analytics and applied statistics.

Responsibilities

• Define and execute the roadmap for agentic AI and digital workers in Finance.
• Lead cross-functional teams with Finance, Product, Engineering, and Operations.
• Architect and scale LLM agents for finance workflows using advanced techniques.
• Develop robust data and inference pipelines in Python/SQL and integrate with APIs and cloud platforms.
• Drive research into Gen AI, agentic AI, and autonomous workflow patterns.
• Mentor teams and deliver enablement materials and best practices for AI adoption.

Job description

JPMorgan Chase’s 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; hands‑on experience with LLMs.
• Strong Python and SQL;
• Practical knowledge of RAG, prompt engineering, fine‑tuning, 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 multi‑agent 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 infrastructure‑as‑code.
• 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.