For LSEG Data Analyst candidates, one report describes a broad technical screen with Python, SQL, ML and LLM concepts, live coding, then a finance-aware face-to-face discussion.
$78K
Avg. Base Comp
$104K
Avg. Total Comp
2 rounds
Typical Rounds
2-4 weeks
Process Length
One candidate’s LSEG Data Analyst interview began with a broad technical round: multiple-choice questions on Python, SQL, machine learning, and LLM-related concepts, followed by live coding in front of the interviewer. Prepare to explain your reasoning while coding, not only to produce a working answer. The candidate also recalls being asked how ML models work, pointing to conceptual explanation as well as practical fluency.
A later face-to-face conversation shifted toward the candidate’s resume and basic knowledge of LSEG, its business, and history. Finance fundamentals were relevant in that discussion: P&L and balance-sheet questions came up. LSEG operates in financial-markets infrastructure and data, so being able to connect analytical work to those business concepts can make your preparation more relevant.
This guide is based on one candidate report, so exact sequencing and coverage may vary. Rehearse concise explanations of Python and SQL choices, refresh the ML concepts you can explain clearly, and be ready to discuss your own experience alongside introductory financial statements.
Synthesized from 2 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 | |
|---|---|
| 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 | |
| Department Expenses | |
| Last Transaction | |
| Maximum Profit | |
| Rain in N Days | |
| Like Tracker | |
| Button AB Test |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a first technical round containing multiple-choice questions across Python, SQL, machine learning, and LLM-related topics. They specifically recall being asked to explain how ML models work, so candidates may benefit from practicing clear conceptual answers rather than relying only on syntax.
The same candidate says the technical round included a live coding exercise projected in front of the interviewer. Candidates should be prepared to narrate their approach, clarify assumptions, and explain their solution as they work; the report does not identify a specific programming task.
A later face-to-face round reportedly covered the candidate’s resume, basic questions about LSEG and its history, and finance concepts including P&L and balance sheets. Review how your analytical work connects to business context and be ready to discuss those experiences conversationally.
