LSEG Data Analyst candidates describe a technical screen, such as MCQs, live coding or a home test, followed by finance and company-knowledge questions. Prepare SQL, Python, market basics and a clear answer to why LSEG.
$70K
Avg. Base Comp
$85K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Three candidates who interviewed for LSEG Data Analyst roles describe a technical filter, and later conversations leaned on finance. That makes this more than a pure SQL-and-Python interview.
One candidate faced technical MCQs covering Python, SQL, ML and LLM topics, then live coding in front of the interviewer on a projector. They were also asked to explain how ML models work. Another received a home test that had to be passed to continue. It mixed SQL, analytics problems and financial questions on markets and trading. A third, from a campus drive, reported a 30-question, 30-minute aptitude test on logic and pseudocode, which they did not clear.
Later conversations leaned on domain knowledge. Reports mention P&L, the balance sheet, bonds, arbitrage, LSEG's products such as Workspace, and its business lines. One candidate also described an all-day group session with several interviewers and a group project.
Adjacent analyst interviews were lighter on technical content. They stressed behavioral answers, a clear "Why LSEG?", and basic SQL or Excel. Outcomes varied across all the reports: offers, a rejection at the aptitude test, an offer declined, and one case of silence after interviews.
Practice explaining your reasoning aloud while you code. Review basic finance vocabulary, and prepare specific examples of data projects from your resume.
Synthesized from 7 candidate 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 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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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Lseg (london stock exchange group)
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Triplet Counting | |
| Hurdles In Data Projects | |
| Deciding Between Solutions | |
| Testing Constraints | |
| Client Solution Pushback | |
| 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 | |
| Experiment Validity | |
| Slacking Employees Salaries | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| Maximum Profit | |
| 500 Cards | |
| Compute Deviation | |
| Subscription Overlap | |
| Button AB Test | |
| Prime to N | |
| Google Maps Improvement | |
| Size of Joins | |
| Session Difference | |
| P-value to a Layman |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports an initial recruiter phone call that was mostly behavioral, covering career goals, data experience and interest in the role. Adjacent analyst candidates mention recorded video or HR screens. Prepare a concise story and a specific answer to why LSEG.
Candidates report different formats: MCQs on Python, SQL, ML and LLM topics; a home test mixing SQL, analytics and finance; and a 30-minute pseudocode aptitude test. One candidate said the home test had to be passed to continue. Expect a filter, but the exact format may vary.
One candidate wrote code live on a projector in front of the interviewer and was also asked how ML models work. Another mentions a coding round but gave no detail. Practice narrating your logic aloud, since clarity under observation appears to matter as much as the answer.
Candidates report questions on LSEG's business and history, resume details, P&L, the balance sheet, bonds, arbitrage and products like Workspace. One also described an all-day group session with a group project. Review finance basics and prepare data project examples.
