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Data Scientist - Senior Associate - Applied AI & Machine Learning

JPMorgan Chase
London, GBR
Full-time
Senior
Onsite
Discovered 2 weeks ago
opentelemetrypythonsql

Full Job Posting

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

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