Data Analyst

Overview

Be part of a small team that designs and implements analytical tools to bring enterprise statistical computing to sell-side equity research. This role involves contributing technically and creatively to the development of a shared ecosystem enabling stock analysts and other stakeholders to build, use, and share reproducible, scalable, and collaborative data analysis workflows. It also includes helping drive the development of LLM-powered tools and Agentic AI workflows to support hypothesis-driven empirical research. You will propose, design, maintain, and support core utilities upholding a \’live data analysis\’ paradigm, enabling dynamic, real-time collaboration across diverse stock research and data analysis teams. You will build and maintain applications that make hypothesis-driven empirical research accessible and actionable for stock analysts. You will provide one-on-one coaching and run workshops to train investment professionals on the enterprise statistical computing platform (built on Python) for managing and analysing data as part of stock analysis. You will collaborate with data and technology partners across the firm to integrate research workflows with enterprise systems.

This role is within the Empirical Scientific Approaches (Global Research) team, centered on bringing hypothesis-driven empirical methods and enterprise statistical computing practices to sell-side equity research. This is an opportunity to be part of a dynamic, creative, globally distributed team at the forefront of equity research as a Data Scientist/Engineer.

Responsibilities

• Be part of a small team that designs and implements analytical tools to bring enterprise statistical computing to sell-side equity research
• Contribute technically and creatively to the development of a shared ecosystem enabling stock analysts and other stakeholders to build, use, and share reproducible, scalable, and collaborative data analysis workflows
• Help drive the development of LLM-powered tools and Agentic AI workflows to support hypothesis-driven empirical research
• Propose, design, maintain, and support core utilities upholding a \’live data analysis\’ paradigm, enabling dynamic, real-time collaboration across diverse stock research and data analysis teams
• Build and maintain applications that make hypothesis-driven empirical research accessible and actionable for stock analysts
• Provide one-on-one coaching and run workshops to train and orient investment professionals on the use of Global Research\’s enterprise statistical computing platform (built on Python) for managing and analysing data as part of their approach to stock analysis
• Collaborate with data and technology partners across the firm to integrate research workflows with enterprise systems

Qualifications

• You will be working in the Empirical Scientific Approaches (Global Research) team. We are bringing hypothesis-driven empirical methods and enterprise statistical computing practices to sell-side equity research. This is an opportunity to be part of a dynamic, creative, globally distributed team positioned at the centre of one of the top equity research departments in the world. As a Data Scientist/Engineer, you will be helping to establish the next frontier of sell-side equity research.
• Proven experience applying software development principles (e.g., modularity, reproducibility, testing) to empirical research workflows
• Proven track record of successful development of applications using an object-oriented programming paradigm, preferably in Python
• Strong foundation in statistical modelling, causal inference, or machine learning
• Intellectual curiosity and excellent communication skills, with a track record of working effectively with technical and non-technical audiences
• Formal training in empirical methods, preferably within an empirical social science discipline (e.g., economics, quantitative sociology, statistics). In exceptional circumstances, equivalent professional experience with on-the-job training can substitute for educational credentials.
• Hands-on experience designing or deploying LLM-based systems (e.g. retrieval-augmented generation, prompt engineering, or evaluation frameworks) would be a major plus