Senior Quant Research Engineer: AI Tools & Data Pipelines

London Stock Exchange; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

London Stock Exchange seeks a highly technical Senior Analyst, Quantitative Research & Development to solve problems across development, data analysis, research and delivery. You will own problems from investigation through resolution while working across multiple teams and disciplines.

Responsibilities include developing Python/SQL tools, validating analytics, and building AI-enabled automation for research and operations. Strong knowledge of equity markets and QA culture is essential.

Qualifications

• 5 years of experience in quantitative development, financial data analytics or related field.
• Strong hands-on Python and SQL skills.
• Good understanding of equity markets and quantitative techniques (regression, optimisation, factor investing, risk modelling).
• Rigorous approach to financial data and QA; experience with pandas, NumPy, Git, testing, CI/CD, cloud computing.

Responsibilities

• Develop, test and maintain Python and SQL‑based research tools, analytics and data pipelines.
• Perform data analysis, validation and QA across large financial datasets; investigate anomalies.
• Develop automation and AI-enabled tools to improve research and operational workflows.
• Support index research, methodology development and rebalances; assist client-facing analyses.
• Collaborate with Research, Engineering and Data Operations to translate requirements into scalable solutions.

Skills

Python

SQL

Data analysis

Pandas

NumPy

Git

CI/CD

Cloud computing

Generative AI

AI agents

Tools

Worldscope

IBES

Lipper

Datastream

Refinitiv Workspace

Job description

London Stock Exchange seeks a highly technical Senior Analyst, Quantitative Research & Development to solve problems across development, data analysis, research and delivery. You will own problems from investigation through resolution while working across multiple teams and disciplines.

Responsibilities include developing Python/SQL tools, validating analytics, and building AI-enabled automation for research and operations. Strong knowledge of equity markets and QA culture is essential.