Two candidates describe Micron Data Scientist interviews built around SQL, deep resume and project questions, and ML basics. One also got a take-home analysis report. Both ended without an offer.
$115K
Avg. Base Comp
$140K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
Micron Data Scientist interviews, as two candidates describe them, center on SQL and a detailed walkthrough of your own projects. Both candidates ended without an offer. One said the role was cancelled partway through, so the result may not reflect their performance.
One candidate began with an online assessment of three fairly basic SQL queries, plus multiple-choice questions on C programming output and aptitude. The other did a live session with five or six medium-level SQL queries, explaining their reasoning as they worked. Practice both basic and medium queries, and be ready to narrate your approach.
Resume depth came up in both accounts. One candidate said the interviewer dug into a project from start to finish with follow-up questions. The other was asked about machine learning applied to manufacturing processes. Prepare two or three projects where you can explain your own role, your decisions, and what you learned. If you have manufacturing-related work, it may be worth preparing to discuss, since one interviewer showed particular interest in it.
Technical questions beyond SQL were fairly basic. One candidate was asked about Python, classification concepts, and some statistics. The other had a second round on ML and project expertise that included an HR component. Both were asked about teamwork or longer-term goals, such as handling conflict or where you see yourself in five years.
One candidate also received a dataset after the interview and was asked to write a data analysis report. If that happens, document your assumptions and findings clearly. Neither candidate gave an exact end-to-end timeline. One described the process as spanning a few weeks.
Synthesized from 2 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 Micron Technology process.
Process: applied online → shortlisted in just 2 days → take-home assignment → one 1-hour video interview (likely with the lead data scientist). Only one interview round before things went quiet.
The take-home was the real core. Scenario: a photolithography area problem with multiple tools, Poisson arrivals, stochastic processing times, etc. You had 3 days (expected effort 2–3 hours) to propose and partially implement a dispatching approach, justify it against an alternative, and explain how you'd validate it with discrete event simulation.
The interview was almost entirely a deep dive into my submission: why I chose that approach, how I validated the simulator, and how the method would extend.
Tip: treat the take-home like a mini consulting report. They explicitly said you'll be asked to explain and extend it live, so know every number and assumption in it.
Questions asked: The interview was almost entirely a deep dive into my take-home assignment: they asked me to explain my approach and the reasoning behind my choices in detail. Unfortunately, I can't share the specific questions or the assignment content, as they are covered by a confidentiality obligation.
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 Micron Technology
How does random forest generate the forest and why use it over logistic regression
| Question | |
|---|---|
| Random Forest from Scratch | |
| Decision Tree Evaluation | |
| Overfit Avoidance | |
| Hurdles In Data Projects | |
| Precision and Recall | |
| Xgboost vs Random Forest | |
| Random Forest Expansion | |
| Digitizing Student Test Scores | |
| Merge Sorted Lists | |
| Find the Missing Number | |
| Scrambled Tickets | |
| Get Top N Frequent Words | |
| Random Weighted Driver | |
| Find the First Non-Repeating Character in a String | |
| Maximum Profit | |
| Assumptions of Linear Regression | |
| Three Zebras | |
| Shortest Path Algorithms | |
| Bias vs. Variance Tradeoff | |
| Using R Squared | |
| One Element Removed | |
| The Brackets Problem | |
| Missing Housing Data | |
| Why Do We Need Time Series Models? | |
| Level Of Rain Water In 2D Terrain | |
| Inactive Users | |
| Mouse Search | |
| Regularization and Validation | |
| Food Delivery Times |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports an online assessment with three basic SQL queries and multiple-choice questions on C programming output and aptitude. The other candidate started with a live introduction instead. The format may vary by role or time, so confirm with your recruiter.
Both candidates report detailed resume discussion. One walked through a project end to end under follow-up questions. The other was asked about machine learning in manufacturing processes. Expect to defend your specific contributions and decisions.
One candidate solved five or six medium-level SQL queries live, then answered Python, classification, and statistics questions. The other describes a second round focused on ML and project expertise. Prepare to explain your reasoning aloud.
Candidates report questions about teamwork and handling conflict, as well as longer-term goals such as where they see themselves in five years. One account says an HR component was mixed into the second technical round.
One candidate was sent a dataset after the initial interview and asked to produce a data analysis report. Both candidates report no offer, and one says the role was cancelled partway through the process.
