Data Scientist - Senior Associate - Applied AI & Machine Learning in London

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 controlBuild and scale AI applications and automated workflows using models, retrieval, and tools, with orchestration for state management and human oversightDevelop context-management and retrieval-augmented generation (RAG) capabilities, including ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputsCreate reliable data and knowledge pipelines that transform enterprise content into high-quality inputs for AI applicationsEstablish engineering standards for reusable tools and model integrations, including interfaces, permissions, testing, failure handling, and documentationImplement evaluation and end-to-end observability for AI systems, optimizing for quality, groundedness, task completion, latency, token usage, cost, reliability, and business impactPartner with portfolio managers and research teams to understand investment processes and translate them into practical solutionsRequired Qualifications, Capabilities, and Skills:Hold a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or a quantitative fieldDemonstrate a strong foundation in statistics, probability, experimental design, and machine learning, with sound judgment in method selection and interpretationPossess 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 equivalentApply practical knowledge of prompt and context design, embeddings, vector and keyword search, reranking, model selection, and optimization for quality, latency, and costExhibit strong Python and SQL skills, with experience using common data and machine learning libraries and frameworksShow numerical intuition and understanding of financial markets, investment research, portfolio construction, risk, performance, and investment dataTranslate ambiguous business requirements into scalable technical solutions and communicate technical trade-offs to stakeholdersDeliver generative AI or machine learning solutions in a regulated enterprise environmentPreferred Qualifications, Capabilities, and Skills:Bring front‑office or buy‑side experience, especially in investment research, portfolio analytics, performance attribution, or decision analyticsIncorporate unstructured or alternative data into research and production workflowsDemonstrate familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and servicesApply 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 stepsShow familiarity with knowledge graphs, multimodal models, fine‑tuning, synthetic data, or advanced model‑evaluation techniquesApply experience with time‑series analysis, forecasting, and quantitative researchHold or be progressing toward the CFA designation
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