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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Lseg (london stock exchange group) process.
The MCQ was my first round, focusing on Python, SQL, Machine learning, NLP. In the face-to-face, they skipped the project highlights and directly attacked my code, demanding proper justification for everything.
Questions asked: They asked me to find the triplets given me with a target value, find the second highest salary
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Topics based on recent interview experiences.
Featured question at Lseg (london stock exchange group)
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Deciding Between Solutions | |
| Triplet Counting | |
| Testing Constraints | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Slacking Employees Salaries | |
| Experiment Validity | |
| Find the Missing Number | |
| Compute Deviation | |
| Bagging vs Boosting | |
| Subscription Overlap | |
| Prime to N | |
| 500 Cards | |
| Session Difference | |
| Last Transaction | |
| Department Expenses | |
| Maximum Profit | |
| Rain in N Days | |
| Like Tracker | |
| Button AB Test |
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.