LSEG Data Scientist candidates report varied paths, from a manager screen and four-day take-home to a five-stage loop with coding, ML work, presentations, statistics, and product-fit discussion.
$107K
Avg. Base Comp
$117K
Avg. Total Comp
5 rounds
Typical Rounds
2-4 weeks
Process Length
LSEG Data Scientist interviews reported here range from a single senior-data-scientist coding interview to a five-stage sequence, so applicants should prepare for different team-specific routes rather than assume one fixed loop. One candidate described a brief introduction followed quickly by three practical Codility tasks. The work was framed less like standard LeetCode and more like implementing logic inside supplied Python functions; TF-IDF-style NLP work, Python, and pandas were specifically named.
Another candidate reported an initial manager conversation focused on resume alignment, use of AI in real work, and experience with factors or financial-data research. Their next stage was a take-home due in four days, using Python and data visualization in a sustainability-focused context, with ML and SQL questions alongside the project. A separate five-stage account included an AI video screen, coding test, small-dataset take-home ML assignment using revenue information, two presentations, and a final product-manager fit discussion.
For the take-home and presentations, be ready to explain why you chose an approach, not only to show an output: candidates reported detailed follow-up on the reasoning behind strategy choices. Refresh statistics as well as behavioral examples, including how you navigate workplace dynamics. The available reports are limited and describe different teams, but together they point to practical implementation, defensible analysis, and clear communication.
Synthesized from 3 candidate reports by our editorial team.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report either an AI video screening or an initial conversation with the prospective manager. The manager discussion focused on resume alignment, practical AI use, and whether the candidate had relevant financial-data or factor-research experience.
One senior-data-scientist interview moved from a brief introduction into three Codility tasks. The candidate described practical implementations rather than typical LeetCode prompts, including TF-IDF-style NLP logic in provided Python function definitions; Python and pandas may be useful.
Candidates report take-home work with a small dataset. One account had four days to complete Python and data-visualization work in a sustainability context, with ML, Python, and SQL discussion; another involved building a classic ML model from revenue data.
In one five-stage process, candidates gave separate presentations to the team and a senior manager. Follow-up questions reportedly tested the reasoning behind strategy choices, so be prepared to explain tradeoffs and the logic supporting an analysis.
One candidate described a final conversation with product managers focused on team fit. Behavioral questions may include how you handle office politics, while other routes ended earlier after the take-home.
