
What to expect in a Merck data scientist interview: behavioral and project discussion, basic machine learning such as overfitting, and, in one intern report, SQL and pandas tests plus a defect-tracking case study.
$133K
Avg. Base Comp
$150K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
Merck's data science interviews, as described in two candidate reports, put as much weight on communication and project depth as on coding. One candidate who interviewed for a Data Scientist role had two rounds, one with the hiring manager and one with the head of data science. Both mixed technical and behavioral questions. They were asked for a success story, a walkthrough of project work, a few puzzles, and an explanation of a scientific concept to a non-scientist. They also got a basic machine learning question about overfitting and how to reduce it. The candidate said it was not a heavy coding or whiteboard interview, and they did not receive an offer.
A data science intern reported a more structured path that ended in an offer. It began with a recruiter screen, then an MCQ aptitude round and a coding test using pandas and SQL. Technical interviews followed, covering feature engineering, logistic regression, outlier detection, CNN filter sizes and estimation puzzles. The final panel of about an hour combined technical depth, behavior and a defect-tracking business case. That candidate stressed asking clarifying questions before answering the case.
For preparation, rehearse two or three projects in depth, including the features you engineered and why you made key decisions. Practice plain-language explanations of technical or scientific ideas, review overfitting and regularization, and keep SQL and pandas sharp in case your role includes a coding screen. If you get a case like defect tracking, start with clarifying questions and then outline your approach end to end rather than jumping straight into code.
Synthesized from 4 candidate reports by our editorial team.
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| Question | |
|---|---|
| Second Longest Flight | |
| Overfit Avoidance | |
| Monthly Customer Report | |
| Cumulative Distribution | |
| Experiment Validity | |
| Last Transaction | |
| Weighted Keys | |
| Hurdles In Data Projects | |
| Always Excited Users | |
| Retailer Data Warehouse | |
| Brain Cancer Treatment Outcomes | |
| Total Spent on Products | |
| P-value to a Layman | |
| RMS Error | |
| Reducing Error Margin | |
| Detecting ECG Tachycardia Runs | |
| Size of Joins | |
| Fair Coin | |
| Cumulative Reset | |
| Causal Email Journey | |
| Time Difference | |
| Greatest Common Denominator | |
| Random Forest Explanation | |
| Subscription Retention | |
| Secret Wins | |
| Sum to Zero | |
| Missing Housing Data | |
| Valid Anagram | |
| Xgboost vs Random Forest |
Synthesized from candidate reports. Individual experiences may vary.
One data science intern candidate reported an initial recruiter screen that mostly confirmed background and interest in the role, and described it as straightforward. The Data Scientist report did not mention a separate screen. If you have one, expect to summarize your experience and motivation concisely.
One data science intern candidate reported an MCQ-based aptitude round followed by a coding test using pandas and SQL. The Data Scientist report did not mention a coding test, so this stage may vary by role and level. Prepare for data manipulation and basic queries under time pressure.
The Data Scientist candidate describes two rounds with the hiring manager and the head of data science, mixing technical and behavioral topics. Reported questions included a success story, project walkthroughs, puzzles, explaining science simply and overfitting. The intern also reported feature engineering, logistic regression and outlier detection questions.
The intern candidate described a final panel of about an hour covering technical depth, a defect-tracking business case and behavioral fit. That candidate said asking clarifying questions before answering mattered. Other roles may end differently, so confirm the format with your recruiter.