Lseg Data Scientist interview typically runs 5 rounds: AI video screening, coding test, take-home assessment, team and senior manager presentations, final product manager fit interview. It usually takes about 2-4 weeks and is notably practical, with a heavy emphasis on hands-on work.
$54K
Avg. Base Comp
$157K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
Our candidates report that LSEG is looking for more than a capable generalist; they want someone who can connect data work to a very specific financial context. A recurring theme is the emphasis on AI usage, factor creation, and financial data research. In one experience, the manager spent a lot of time probing whether the candidate had built their own factors before, which suggests the bar is not just “can you model?” but “can you reason like someone who understands market data and the business problem behind it.”
We’ve also seen that the technical work here is judged through a practical lens. The take-home and presentation feedback point to a preference for candidates who can explain why they chose a strategy, not just execute a standard workflow. One candidate noted that reviewers asked detailed follow-ups to test the reasoning behind their approach, while another described a hands-on assignment centered on Python, visualization, ML, and SQL with a sustainability angle. That combination tells us LSEG cares about clear analytical judgment under domain constraints as much as raw technical output.
The non-obvious make-or-break factor is pace and completeness. One candidate’s process ended because the assignment wasn’t finished on time, which is a strong signal that delivery discipline matters here. Across the experiences, we also see a steady interest in whether candidates can operate across the stack and speak credibly about the team’s subject area, whether that’s sustainability or revenue-driven financial analysis. In short, our candidates report that LSEG favors people who can translate data work into business-relevant decisions and defend those choices with confidence.
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 first conversation was an initial screening call with the manager I would have reported to, and it felt pretty focused on whether my background matched the core requirements of the role. A lot of the discussion was around my resume and the parts of my experience that lined up with the job, especially AI usage, since the role seemed to involve heavy use of it. I was also asked about whether I had worked on creating my own factors with financial data research, which I hadn’t done directly, but I tried to show that I could get up to speed on it quickly.
After that, the next round was a take-home interview that had to be completed in four days. That one was centered on Python and data visualization, with the team’s main focus in my case being sustainability. There were also questions around ML, Python, and SQL, so it wasn’t just a pure coding exercise — it was more about showing that I could work with the stack and think through the team’s domain. The take-home required a confidentiality agreement, so I couldn’t share the details, but it definitely felt like the most substantive part of the process. I wasn’t able to finish it on time, and that was the end of the process for me. Overall, it seemed like a fairly practical interview loop that tested both fit and hands-on ability, especially around AI, Python, SQL, and domain-specific data work.
Prep tip from this candidate
Be ready to discuss how you’ve used AI in real work, and prepare to explain any experience you have with building factors or doing financial data research. For the take-home, practice Python and data visualization in a sustainability context, and make sure you can answer basic ML and SQL questions alongside the project.
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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 | |
| Testing Constraints | |
| Triplet Counting | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
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| Rolling Bank Transactions | |
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| Closest SAT Scores | |
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| Monthly Customer Report | |
| Experiment Validity | |
| Find the Missing Number | |
| Compute Deviation | |
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| String Shift | |
| Prime to N | |
| 500 Cards | |
| Session Difference | |
| Last Transaction | |
| Top 5 Turnover Risk | |
| Maximum Profit | |
| Rain in N Days |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with either an automated AI video screening or a live conversation with the hiring manager. When it is a manager-led screen, the discussion focuses on resume fit, AI usage experience, and familiarity with financial data research such as factor creation.
Candidates complete a coding test covering core technical skills including Python, SQL, and statistics questions relevant to data science work in a financial data context.
Candidates receive a take-home assignment to be completed within four days, sometimes requiring a confidentiality agreement. The work involves Python, data visualization, ML modeling, SQL, and domain-specific analysis such as sustainability or financial revenue data.
Candidates present their take-home results in two separate sessions — one to the team and one to a senior manager. Interviewers probe the reasoning behind modeling and strategy choices, expecting candidates to defend their analytical decisions rather than simply citing standard methods.
The final round is conducted with product managers to assess cultural and team fit. Candidates can expect behavioral questions, including scenarios around handling office politics and collaborating within cross-functional teams.