Lseg (london stock exchange group) Data Scientist Interview Guide

Candidates describe the LSEG Data Scientist interview as hands-on, but the three reports do not describe the same loop, so it helps to prepare for several formats rather than one script.

One candidate went through five rounds: an AI video screening, a coding test, a take-home built on a small dataset, two presentations (one to the team, one to a senior manager), and a final conversation with product managers about team fit. The take-home supplied company information such as revenue data and asked for a classic ML model. In the presentations, interviewers pressed on the reasoning behind each strategy choice rather than accepting standard norms. Statistics and behavioral questions, including how to handle office politics, also came up.

Another candidate started with a call with the prospective manager, centered on resume fit, AI usage and whether they had built their own factors from financial data research. A take-home with a four-day window followed, focused on Python and data visualization in a sustainability setting, with ML and SQL questions alongside it. That candidate ran out of time on the take-home, and the process ended there with no offer.

A third candidate, interviewing for a senior data scientist role, described one round that was nearly all coding: three Codility tasks, with hints and function stubs, asking for NLP algorithms such as TF-IDF. They called the tasks practical rather than LeetCode-style and said strong Python and pandas helped. They did not solve the tasks and expected a rejection.

Practice writing pandas and Python inside given function scaffolds, time yourself on a take-home, and rehearse explaining your modeling choices out loud.