Data Scientist - Senior Associate - Applied AI & Machine Learning
Salary not listed • LONDON, United Kingdom • Full-time
Posted 23d ago
Job Location
LONDON, United Kingdom
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
JPMorgan Chase is seeking an Applied AI & Machine Learning Senior Associate to join their Asset Management Data Science Team in London. This role will focus on building the technical foundation for intelligent investor tools, leveraging advanced analytics and generative AI solutions. You will design context-management capabilities, develop LLM-powered applications, and create reliable pipelines for enterprise and investment data. The position involves partnering with investment, data science, engineering, product, and control teams to transform complex financial workflows into secure, scalable solutions within a globally connected team.
Responsibilities
- Design and implement scalable architecture for LLM-powered investment tools, including 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, covering ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputs.
- Create reliable data and knowledge pipelines to 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.
Requirements
- Master's degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or a quantitative field.
- Strong foundation in statistics, probability, experimental design, and machine learning, with sound judgment in method selection and interpretation.
- 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.
- Practical knowledge of prompt and context design, embeddings, vector and keyword search, reranking, model selection, and optimization for quality, latency, and cost.
- Strong Python and SQL skills, with experience using common data and machine learning libraries and frameworks.
- Numerical intuition and understanding of financial markets, investment research, portfolio construction, risk, performance, and investment data.
- Ability to translate ambiguous business requirements into scalable technical solutions and communicate technical trade-offs to stakeholders.
- Experience delivering generative AI or machine learning solutions in a regulated enterprise environment.