Data Scientist - Senior Associate - Applied AI & Machine Learning

London Source: WhatJobs

See advert for pay

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