Data Scientist - Associate

J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.​

You are a strategic thinker passionate about driving solutions in “Applied AI & Machine Learning”. You have found the right team.

As “Applied AI & Machine Learning Professional” within our “Asset Management Data Science Team ” , you will be responsible for setting and improving our organizational objectives, and ensuring their consistent accomplishment.

Job Responsibilities

• Design and implement scalable architecture for LLM-powered investment tools, including reusable patterns for integration, governance, observability, and access control
• Build and scale AI applications and automated workflows using models, retrieval, and tools with orchestration for state management, retries, checkpointing, deterministic controls, and appropriate 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 structured and unstructured enterprise content into governed, 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, including structured logging and tracing across model calls, retrieval steps, and tool execution
• Optimize AI systems for quality, 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 within the firm’s ecosystem

Required qualifications, capabilities and skills

• Earn a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or another quantitative field
• Apply a strong foundation in statistics, probability, experimental design, and machine learning to select appropriate methods and interpret quantitative results
• Build, deploy, and scale 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
• Design prompts and context effectively, including embeddings, vector and keyword search, reranking, model selection, and quality/latency/cost optimization
• Write strong Python and SQL, using common data and machine learning libraries and frameworks
• Demonstrate sound 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 a range of stakeholders

Preferred qualifications, capabilities and skills

• Deliver generative AI or machine learning solutions in a regulated enterprise environment
• Bring front-office or buy-side experience, particularly in investment research, portfolio analytics, performance attribution, or decision analytics
• Incorporate unstructured or alternative data into research and production workflows
• Apply familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and services
• Use 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)
• Leverage familiarity with knowledge graphs, multimodal models, fine-tuning, synthetic data, or advanced model-evaluation techniques
• Pursue CFA qualification or progress toward the CFA designation