What Leidos Data Analyst candidates report: conversational recruiter and analyst screens focused on resume and fit, plus one report of a take-home data project. Includes prep tips and a salary estimate.
$122K
Avg. Base Comp
$157K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Leidos Data Analyst interview as more conversational than technical. One candidate began with a relaxed recruiter phone screen covering their resume, strengths and weaknesses, then did a video interview with a current analyst that kept the behavioral and resume-driven focus. That candidate did not advance and said the interviews felt less technical than expected for the role.
A separate 2025 candidate reported something different: two virtual meetings with the hiring manager, then a take-home project involving data transformations on non-standard file types. The candidate submitted a written comparison of three approaches, and the reviewer then asked for full code, Git history and documentation. The candidate heard nothing after resubmitting. Since this is one report, treat it as a reason to ask questions, not as a guaranteed step.
A third candidate, whose listing was a Senior Network Analyst role, described three interviews that centered on school projects, comfort with government-sector work and a few oddball questions. Basic Python came up because the candidate had scripts on GitHub, and that candidate received an offer.
For preparation, be ready to explain each resume item and project in detail, and tie your strengths and weaknesses to specific examples. If a take-home arrives, ask whether the team wants a high-level analysis or production-ready code with documentation. Brush up on basic scripting you have published, and practice talking through unexpected questions aloud. Reports are few and varied, so expect the format to differ by team.
Synthesized from 3 candidate reports by our editorial team.
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Featured question at Leidos
Write a function `sorting` from scratch to sort a list of strings in ascending alphabetical order
| Question | |
|---|---|
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| KNN From Scratch | |
| Your Strengths and Weaknesses | |
| Backpropagation Explanation | |
| First Touch Attribution | |
| Swipe Precision | |
| Project Budget Error | |
| P-value to a Layman | |
| Same Algorithm Different Success | |
| Employee Project Budgets | |
| Forecasting New Year Revenue | |
| Skewed Pricing | |
| Classification and Regression | |
| Slow SQL Query | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Bias vs. Variance Tradeoff | |
| Liker's Likers | |
| Seller Type Modeling | |
| Why Do You Want to Work With Us | |
| Client Solution Pushback | |
| PCA and K-Means | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Employee Salaries |
Synthesized from candidate reports. Individual experiences may vary.
One candidate describes a laid-back phone screen with a recruiter covering general background, resume details, and top strengths and weaknesses. It was described as conversational. Be ready to explain specific resume items and tie strengths and weaknesses to real experience.
Candidates report video or virtual conversations with a current analyst or the hiring manager. One described follow-ups on resume projects and little technical depth. Another described an introductory hiring manager meeting. Prepare concrete project stories and, for government-facing work, comfort with that environment.
One 2025 candidate was given a take-home on data transformations with non-standard file types. They initially wrote a comparison report and were then asked for full code, Git history and documentation. This may not apply to every team, so clarify the expected deliverable up front.
One candidate was told a final round with team managers would follow the initial rounds, and another met several team members and was told about a client meeting. The format likely varies by team. Expect fit, communication, basic scripting and oddball questions rather than deep analytics.
