Three candidates describe Applied Materials Data Scientist interviews: a peer phone screen, a 24-hour take-home case study, project deep dives, SQL and Python coding, behavioral questions and supply chain questions.
$142K
Avg. Base Comp
$165K
Avg. Total Comp
Not reported
Typical Rounds
2 weeks
Process Length
Applied Materials Data Scientist interviews, as three candidates describe them, differ quite a bit in format, so it helps to prepare across coding, modeling and conversation.
The candidate who received an offer started with a 30-minute phone screen with a peer covering statistics, a straightforward coding problem and some SQL. A take-home case study with a 24-hour window came next, pairing a medium-level LeetCode-style coding question with technical and design problems. A one-hour technical interview then centered on past projects, design decisions in a system they had built, and how they would approach model design for a real problem. Interviewers in that round wanted to understand how the candidate thought about defending your trade-offs and scale. This candidate reports roughly two weeks from first contact to a final panel that included future teammates.
A second candidate, who did not get an offer, worked through advanced SQL, Python coding and Power BI and heard positive feedback from the interviewers before the role was put on hold.
A third candidate, who was rejected, had a resume deep dive, about five behavioral questions with follow-ups and basic ML questions. They were also asked about the pillars of supply chain, what RAG is and the steps in an ML pipeline. Much of the time went on explaining the models and tech stack from their own projects, including why they chose them and how they implemented them.
Practice explaining why you picked each model, keep your SQL and Python clean, and be ready to connect your work to supply chain problems.
Synthesized from 4 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Applied Materials process.
The process started with a 30-minute phone screen with a peer from the team, where they asked statistical questions, a straightforward coding problem, and some SQL. That felt like a warm-up—nothing too brutal. What really tested me came next: a take-home case study I had to complete in 24 hours. It included a medium-level LeetCode-style coding question alongside technical and design problems. I spent most of that time thinking through the solution approach rather than rushing code, which felt right given the scope.
After submitting, I had a one-hour technical interview where the conversation shifted from pure algorithm execution to broader context. They dug into my past projects, asked me to walk through design decisions in a system I'd built, and probed how I'd approach model design for a real problem. It wasn't adversarial—more like they wanted to understand how I think about trade-offs and scale. The hiring manager also conducted an initial call earlier in the process to understand my background and whether my hands-on experience aligned with what they needed. The whole thing spanned about two weeks from first contact to final panel, which included members of the team I'd be working with directly. They seemed genuinely interested in my approach to problems rather than just looking for perfect answers.
Prep tip from this candidate
Focus hard on the take-home assessment—it's a real filter. Solve the medium-level coding problem cleanly, but spend equal time on system design and model design sections. Be ready to defend your architectural choices and discuss trade-offs in a technical conversation afterward.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Applied Materials
Describing a data project and its challenges
| Question | |
|---|---|
| Find the Missing Element | |
| Matrix Rotation | |
| Relational Migration | |
| Your Strengths and Weaknesses | |
| Merge Sorted Lists | |
| Find the Missing Number | |
| Scrambled Tickets | |
| Maximum Profit | |
| Find the First Non-Repeating Character in a String | |
| One Element Removed | |
| Precision and Recall | |
| The Brackets Problem | |
| Level Of Rain Water In 2D Terrain | |
| Get Top N Frequent Words | |
| Covariance vs Correlation | |
| Using R Squared | |
| Random Forest Explanation | |
| Random Weighted Driver | |
| Cyclic Detection | |
| Categorize Sales | |
| Same Algorithm Different Success | |
| Three Zebras | |
| Missing Housing Data | |
| Valid Anagram | |
| Finding The Mode | |
| Xgboost vs Random Forest | |
| Dijkstra implementation | |
| Food Delivery Times | |
| Success Measurement |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a 30-minute phone screen with a peer on the team that covered statistics questions, a straightforward coding problem and some SQL. They describe it as a warm-up. The same candidate also had an earlier call with the hiring manager about their background and how well their hands-on experience matched the role.
The candidate who received an offer completed a take-home case study within 24 hours. It combined a medium-level LeetCode-style coding question with technical and design problems. They spent most of the time thinking through the solution approach rather than rushing to write code, and describe this stage as the real test of the process.
After the take-home, the same candidate had a one-hour technical interview about past projects, design decisions in a system they had built, and how they would approach model design for a real problem. Interviewers wanted to understand their trade-off thinking. A final panel followed, including members of the team they would join, about two weeks after first contact.
A candidate who did not get an offer reports advanced SQL, Python coding and Power BI, then a role put on hold. A rejected candidate describes a resume walkthrough, about five behavioral questions, basic ML concepts, supply chain pillars, what RAG is, ML pipeline steps and detailed questions on the models and tech stack from their projects.
