Data Scientist – Senior Associate – Applied AI & Machine Learning

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

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