Senior Associate -Applied AI Data Scientist

• Build production LLM agents for finance workflows using techniques such as retrieval-augmented generation (RAG), tool use, and multi-step reasoning.
• Develop robust data and inference pipelines in Python and SQL; integrate agents with APIs, microservices, and BI applications.
• Implement evaluation frameworks and guardrails: offline and online tests, automatic metrics (factuality, grounding, hallucination rate), human-in-the-loop reviews, red-team testing, and observability.
• Optimize for scale, latency, and cost across cloud environments; leverage vector databases and embeddings for efficient retrieval.
• Partner with Finance, Product, and Engineering to identify high-value use cases; translate ambiguous problems into measurable outcomes.
• Apply solid ML engineering and MLOps practices (versioning, CI/CD, model registry, monitoring, incident response).
• Document systems, deliver enablement materials, and upskill partners; contribute to standards for privacy, security, and model risk governance., * 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.

• 6+ years in data/ML roles, including 3+ 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.
• Eexperience with PySpark or distributed compute.
• 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.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

About the role JPMorgan Chase's Asset & Wealth Management Finance organization is building the next generation of agentic AI solutions that act as "digital workers" for forecasting, analytics, and decision support., JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.