Lseg Data Analyst interview typically runs 4 rounds: technical MCQs, live coding, face-to-face, and resume/company fit. It usually takes a few rounds over a short process and includes an unusual projector-based coding stage.
$89K
Avg. Base Comp
$106K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that LSEG is looking for analysts who can move comfortably across technical depth and business context, not just someone who can write a query. A recurring theme is the breadth of the technical screen: one candidate described being hit with Python, SQL, ML, and even LLM-related MCQs before being asked to code live and explain the logic out loud. That combination suggests they care a lot about conceptual clarity under pressure — if you can’t articulate why a model works or how you’re approaching a problem, the answer itself may not be enough.
We’ve also seen that the company expects finance fluency to be part of the baseline, even for a data analyst seat. Multiple candidate experiences mention questions on P&L, the balance sheet, and the company’s business and history, which tells us they want people who can connect analysis to how a financial institution actually operates. The resume discussion seems to matter too, especially around data cleaning and project hurdles, so they’re likely checking whether you’ve dealt with messy, real-world data rather than only textbook examples.
The non-obvious signal here is that LSEG appears to reward candidates who can stay composed while switching contexts quickly: from technical theory to live implementation to finance fundamentals. Our candidates report that the strongest impression comes from being able to explain tradeoffs clearly and show that you understand the business implications of your work, not just the mechanics. In other words, they seem to value analytical range with commercial awareness.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Lseg (london stock exchange group) process.
The hardest part for me was how broad the first technical round was. I went in expecting a fairly standard data analyst interview, but the process started with technical MCQs covering Python, SQL, ML, and even LLM-related questions, followed by a live coding round where I had to write code in front of the interviewer on a projector. That was a little unusual and definitely added pressure, because it wasn’t just about getting the answer right but also being able to explain my thinking clearly while coding in real time. One of the questions I remember was to explain how ML models work, so they were checking for conceptual understanding rather than just syntax or memorized definitions. After that, the interview felt more conversational in the later stages. I also had a face-to-face round where they asked basic questions about the company, its business, and its history, along with resume-based questions. They specifically asked about P&L and the balance sheet, so it helped to be comfortable with finance fundamentals even for a data role.
Prep tip from this candidate
Be ready for a first-round technical screen that mixes Python, SQL, ML, and LLM MCQs with a live coding exercise in front of the interviewer. Also review LSEG’s business basics and finance terms like P&L and balance sheet, since those came up in the face-to-face round.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Lseg (london stock exchange group)
Describing a data project and its challenges
| Question | |
|---|---|
| Testing Constraints | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Employee Salaries | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Slacking Employees Salaries | |
| Experiment Validity | |
| Find the Missing Number | |
| Compute Deviation | |
| Bagging vs Boosting | |
| Prime to N | |
| 500 Cards | |
| Last Transaction | |
| Department Expenses | |
| Session Difference | |
| Rain in N Days | |
| Button AB Test | |
| Subscription Overlap | |
| P-value to a Layman | |
| Bank Fraud Model | |
| Paired Products | |
| Swipe Precision |
Synthesized from candidate reports. Individual experiences may vary.
The first technical round is broad and starts with multiple-choice questions covering Python, SQL, machine learning, and even LLM-related topics. Candidates should expect conceptual questions such as how ML models work, not just syntax or memorized definitions.
This stage includes coding live in front of the interviewer, sometimes on a projector, which adds pressure and tests both problem-solving and communication. Interviewers look for clear thinking, the ability to explain your approach in real time, and solid coding fundamentals.
Later stages become more conversational and include in-person questions about the company, its business, and its history. Expect resume-based discussion as well as finance fundamentals such as P&L and the balance sheet, even for a data analyst role.