LSEG Data Analyst candidates report a mix of technical screens in Python, SQL and ML, plus finance questions on markets, P&L and balance sheets, and behavioral discussion. Formats vary by hiring route.
$76K
Avg. Base Comp
$95K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Candidates for LSEG Data Analyst roles describe interviews that go beyond a standard analytics screen. One candidate faced technical multiple-choice questions on Python, SQL, ML and LLMs, then wrote code live in front of the interviewer and was asked to explain how ML models work. Another described a home test mixing SQL, analytics problems and financial questions, followed by an all-day group interview with several team sessions and a collaborative group project. A third, from a campus drive, reported a 30-minute, 30-question aptitude test on logic and pseudocode that served as the main filter.
Finance fluency came up in more than one report. Candidates mention P&L and the balance sheet, markets and trading concepts, and LSEG's own business and history. One candidate said the home test downplayed finance but still included it. If you come from a pure tech background, a basic grounding in markets is worth the time.
Behavioral questions appear alongside the technical ones: career goals, why you want the role, and resume-based discussion. Expect to explain your thinking aloud as you work, not just reach an answer. Outcomes in the reports are mixed, so keep other applications moving.
Synthesized from 8 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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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 | |
| Client Solution Pushback | |
| Deciding Between Solutions | |
| Testing Constraints | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Find the Missing Number | |
| Comments Histogram | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Slacking Employees Salaries | |
| Last Transaction | |
| Monthly Customer Report | |
| Payments Received | |
| Experiment Validity | |
| Size of Joins | |
| Paired Products | |
| Bagging vs Boosting | |
| Total Spent on Products | |
| Maximum Profit | |
| 500 Cards | |
| Google Maps Improvement | |
| Get Top N Frequent Words | |
| Top 3 Users |
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 why they wanted the role. Prepare a clear 'Why LSEG?' answer and a concise account of your data background.
Reports describe different formats. One candidate had MCQs on Python, SQL, ML and LLMs, then live coding on a projector. Another had a home test with SQL, analytics problems and financial questions that gated progress. A campus candidate reported a 30-minute pseudocode aptitude test. The format may vary by hiring route, so practice code tracing, SQL and explaining your reasoning.
One candidate reports coding in real time and being asked to explain concepts such as how ML models work, which suggests interviewers may look for conceptual understanding rather than memorized syntax. A campus candidate mentions a separate coding round but gave no details, so treat specifics as unknown.
One candidate reports a face-to-face round covering LSEG's business and history, resume questions, P&L and balance sheet. Another describes an all-day session with multiple team interviews and a group project with real-time feedback. Review LSEG's business, basic markets and finance fundamentals, and practice collaborating out loud.
