Leidos Data Scientist interview typically runs 2 rounds: recruiter Zoom call, then hiring manager and team Zoom interview. It usually moves quickly, often within days, and feels conversational rather than formal.
$120K
Avg. Base Comp
$125K
Avg. Total Comp
2
Typical Rounds
1-2 weeks
Process Length
Our candidates report that Leidos interviews feel much more like a working session than a test. The strongest signal is depth of ownership: one candidate said the team spent a lot of time on a single AI project and wanted the full story end to end, from how it worked to why each decision was made. That tells us they are not just checking whether you’ve touched the right tools — they want to see whether you can explain the tradeoffs, defend your choices, and stay coherent when the conversation moves from resume bullets into implementation details.
A recurring theme is that Leidos seems to value practical judgment over flashy theory. Even with questions like backpropagation and XGBoost vs. Random Forest, the tone described was conversational rather than adversarial, which suggests they’re using technical prompts to gauge whether you can connect fundamentals to real project work. We also see a light but real emphasis on fit and communication, since candidates were asked about strengths and weaknesses alongside their technical background. In practice, the people who do best here are usually the ones who can narrate their work clearly, show how they think through model choices, and make their experience feel directly relevant to the mission-driven environment.
Synthetized from 1 candidates reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Leidos
Write a function `sorting` from scratch to sort a list of strings in ascending alphabetical order
| Question | |
|---|---|
| Hurdles In Data Projects | |
| KNN From Scratch | |
| Xgboost vs Random Forest | |
| Your Strengths and Weaknesses | |
| Backpropagation Explanation | |
| First Touch Attribution | |
| Swipe Precision | |
| Employee Project Budgets | |
| Detecting Firearm Sales | |
| P-value to a Layman | |
| Project Budget Error | |
| Same Algorithm Different Success | |
| String Mapping | |
| Forecasting New Year Revenue | |
| Skewed Pricing | |
| Classification and Regression | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Slow SQL Query | |
| Bias vs. Variance Tradeoff | |
| Liker's Likers | |
| Why Do You Want to Work With Us | |
| Seller Type Modeling | |
| Client Solution Pushback | |
| PCA and K-Means | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with a recruiter Zoom call to cover your background, interest in the Data Scientist role, and overall fit. In this experience, it moved quickly and felt straightforward rather than highly formal or adversarial.
The second round is a Zoom interview with the hiring manager and most of the team you would work with. The discussion focuses on your resume and prior projects, with a deep dive into one AI project where you explain how it worked, how you built it, and the decisions you made.
A major part of the team conversation is walking through a specific AI project end to end. They ask detailed questions about your approach, implementation choices, and the reasoning behind your decisions, making this the most technical portion of the process.
The interview also includes basic behavioral questions, such as strengths and weaknesses, along with questions about previous experience. This stage appears to be more conversational and aimed at understanding how you would work with the team.