Overview
In this role you will design scalable, AI-powered investment tools to support intelligent decision-making and client engagement. You’ll work with cross-functional teams to build context-management, RAG-capable workflows, and reliable data pipelines that transform enterprise content into high-quality inputs for AI systems. You’ll deploy and govern LLM-driven applications within a regulated finance environment, measuring quality, latency, and business impact. This position sits in a small, globally connected team backed by one of the world’s largest financial institutions, offering significant scope to influence investment workflows and data science practices.
Pay / Benefits
- career growth
- mobility
- collaboration across the firm
- global team
- resources of a leading financial institution
Responsibilities
- Design scalable architecture for LLM-powered investment tools with integration, governance, observability, and access control
- Build and scale AI applications and automated workflows using models, retrievals, and tools with state management and human oversight
- Develop context-management and retrieval-augmented generation capabilities (ingestion, embeddings, hybrid search, grounding)
- Create reliable data and knowledge pipelines converting enterprise content into high-quality AI inputs
- Establish engineering standards for reusable tools and model integrations (interfaces, permissions, testing, failure handling, documentation)
- Implement evaluation and end-to-end observability for AI systems (quality, groundedness, latency, cost, reliability, business impact)
- Partner with portfolio managers and research teams to translate investment processes into practical solutions
Key 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
- Hands-on experience building, deploying, and scaling LLM-powered applications (retrieval, tools, orchestration, state management, structured outputs, evaluation) using LangGraph, Semantic Kernel, LlamaIndex, or equivalents
- Practical knowledge of prompt design, embeddings, vector/keyword search, reranking, model selection, and cost/latency optimization
- Strong Python and SQL skills with experience in data and ML libraries/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 trade-offs
- Experience delivering generative AI or ML solutions in a regulated enterprise environment
- communication with stakeholders
- ability to translate business needs into technical solutions
- team collaboration across cross-functional groups
- LLM-powered applications
- retrieval, tool use, workflow orchestration
- state management
Data Scientist - Senior Associate - Applied AI & Machine Learning in London employer: JP Morgan Chase
Morgan is an exceptional employer, offering a dynamic work culture that prioritises diversity and inclusion while fostering employee growth through comprehensive coaching and development opportunities. As a global leader in financial services, we empower our teams to drive impactful product management and AI enablement, ensuring that every employee can contribute meaningfully to our clients' success in a collaborative environment located at the heart of the financial sector.
