LinkedIn Data Analyst interviews may include a remote background discussion, a behavioral prioritization question, and a final presentation, based on one candidate report.
$119K
Avg. Base Comp
$192K
Avg. Total Comp
3
Typical Rounds
3-5 weeks
Process Length
LinkedIn Data Analyst interviews may begin with a conversation about your background and how directly it matches the role. In the available Data Analyst-aligned account, the opening discussion was broad rather than a technical screen: the interviewer asked about relevant experience and used the conversation as a fit check. Prepare a concise account of the work you have done, the context for it, and why it transfers to this position.
The same candidate described a further one-on-one conversation and a presentation, all conducted remotely. A behavioral prompt focused on managing end-of-quarter priorities. Practice explaining tradeoffs under deadline pressure: describe how you identify competing work, decide what moves first, communicate changes, and keep the result tied to the business need. Keep the answer grounded in a real example rather than treating prioritization as an abstract framework.
The presentation was the final stage in that account, so prepare to walk an interviewer through your work and the decisions behind it in a clear sequence. Be ready to explain what you chose to emphasize, what alternatives you considered, and how you would make the material understandable to the intended audience. The available picture is based on one Data Analyst-aligned report, so the exact format may vary. The candidate described the questions as routine and did not report a technical exercise; focus your preparation on clear experience narratives, practical prioritization, and a well-structured presentation.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Linkedin process.
First round was a recruiter round where we discussed the role and explored the general fit. even though this was a data insights role, and I am a Data Scientist, I had analytics experience and they seemed to be okay with that background for this role. My next round was with the Hiring Manager. This was centered around getting to know each other in terms of role and background, more on what the team does and how I may be able to contribute. There were a couple "case" scenarios to discuss (AI related) that I did not expect. And eventually time for answering any questions I may have.
Questions asked: For the HM round: started with my background, what i have worked on before. Specifically if i have worked with LLMs and AI before. this role was centered around AI enablement. The expectation from the role was leading AI efforts to draw insights and add value. Case scenario I was asked was about using call transcripts to draw insights. AI related conversation started when I suggested using AI for this. Questions around how I could implement something like that, what do i need to keep in mind. Also how to evaluate the AI model and ascertain if it is doing a good job.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Linkedin
User Experience Percentage
| Question | |
|---|---|
| 500 Cards | |
| Month Over Month | |
| Over-Budget Projects | |
| Raining in Seattle | |
| Bagging vs Boosting | |
| Network Experiment Design | |
| Delivery Estimate Model | |
| Declining Applicants | |
| Repeat Job Postings | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Lasso vs Ridge | |
| Recruiting Leads | |
| Testing Price Increase | |
| Multi-Reaction | |
| Job Training Program Evaluation | |
| Possible Triangles | |
| Type I and II Errors | |
| Biased Random Number Generator | |
| Unbiased Estimator | |
| Green Dot | |
| Career Jumping | |
| 180 Day Job Postings | |
| Scrapers or Users | |
| Ranking Metrics | |
| Understanding Dynamic Pricing Strategy | |
| Activity Conversion | |
| k-Means from Scratch |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports that the first conversation focused broadly on their background and whether their experience matched the role. Prepare a concise explanation of directly relevant work and how it applies to a Data Analyst position.
The same candidate described later one-on-one rounds that remained straightforward. One reported question asked how they would manage end-of-quarter priorities, so candidates may want a concrete example of organizing work and making tradeoffs under pressure.
The reported process ended with a presentation. Candidates should be ready to clearly walk through their work and the decisions behind it; the report does not specify the presentation topic, format, or evaluation criteria.
