Data Scientist - Senior Associate - Applied AI & Machine Learning (Hiring Immediately)

Are you ready to make an impact at the intersection of finance and technology? At JPMorgan Chase, you’ll help drive innovation in investment decision-making and client engagement. You’ll work with advanced analytics, enterprise data platforms, and generative AI solutions that power our global business. We offer career growth, mobility, and the opportunity to collaborate with talented professionals across the firm. Your skills will help us deliver secure, scalable, and measurable solutions for our clients. As an Applied AI & Machine Learning Senior Associate in the Asset Management Data Science Team, you will build the technical foundation for intelligent investor tools. You will design context-management capabilities, develop LLM-powered applications, and create reliable pipelines for enterprise and investment data. You will partner with investment, data science, engineering, product, and control teams to turn complex financial workflows into secure, scalable solutions. You will join a small, globally connected team with the resources and impact of one of the world’s largest financial institutions. Job Responsibilities: Design and implement scalable architecture for LLM-powered investment tools, ensuring integration, governance, observability, and access control Build and scale AI applications and automated workflows using models, retrieval, and tools, with orchestration for state management and human oversight Develop context-management and retrieval-augmented generation (RAG) capabilities, including ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputs Create reliable data and knowledge pipelines that transform enterprise content into high-quality inputs for AI applications Establish engineering standards for reusable tools and model integrations, including interfaces, permissions, testing, failure handling, and documentation Implement evaluation and end-to-end observability for AI systems, optimizing for quality, groundedness, task completion, latency, token usage, cost, reliability, and business impact Partner with portfolio managers and research teams to understand investment processes and translate them into practical solutions Required Qualifications, Capabilities, and Skills: Hold a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or a quantitative field Demonstrate a strong foundation in statistics, probability, experimental design, and machine learning, with sound judgment in method selection and interpretation Possess hands-on experience building, deploying, and scaling LLM-powered applications, including retrieval, tool use, workflow orchestration, state management, structured outputs, and evaluation, using frameworks such as LangGraph, Semantic Kernel, LlamaIndex, or equivalent Apply practical knowledge of prompt and context design, embeddings, vector and keyword search, reranking, model selection, and optimization for quality, latency, and cost Exhibit strong Python and SQL skills, with experience using common data and machine learning libraries and frameworks Show numerical intuition and understanding of financial markets, investment research, portfolio construction, risk, performance, and investment data Translate ambiguous business requirements into scalable technical solutions and communicate technical trade-offs to stakeholders Deliver generative AI or machine learning solutions in a regulated enterprise environment Preferred Qualifications, Capabilities, and Skills: Bring front-office or buy-side experience, especially in investment research, portfolio analytics, performance attribution, or decision analytics Incorporate unstructured or alternative data into research and production workflows Demonstrate familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and services Apply experience with LLM observability and evaluation standards or platforms such as OpenTelemetry, OpenInference, Arize Phoenix, LangSmith, or equivalent, including traces across model, retrieval, and tool-execution steps Show familiarity with knowledge graphs, multimodal models, fine-tuning, synthetic data, or advanced model-evaluation techniques Apply experience with time-series analysis, forecasting, and quantitative research Hold or be progressing toward the CFA designation