Amgen Data Scientist interviews, as one candidate describes them, run from a CV and screening form to an in-person day of five back-to-back interviews. Expect plain-language statistics questions and real work examples.
$126K
Avg. Base Comp
$160K
Avg. Total Comp
5 rounds
Typical Rounds
Not reported
Process Length
The clearest account of Amgen's Data Scientist interview comes from one candidate who received an offer. They began with a CV submission and screening questions in a form, then were invited to an office a few weeks later for five interviews and assessments in a single day. They reported barely any downtime between sessions, so stamina matters as much as content.
The question this candidate remembers best was explaining mean and standard deviation to someone with no statistics background. They read it as a test of real understanding rather than memorization. Practice giving short, intuitive explanations of core statistical ideas without jargon. The same candidate was also asked for concrete examples from past work, and they said communicating with both technical and non-technical audiences was clearly valued.
A separate report from a Data Science Intern, who also received an offer, describes three rounds of about an hour each over a couple of weeks. It covered an HR screen, a technical round using R and Python, and a behavioral round. The technical round included business-grounded questions such as random forests versus decision trees and how to model a hypothetical dataset. Because that is an internship, treat it as a loose hint about technical depth rather than a description of the full-time loop.
Prepare two or three project stories in which you can explain your modeling choices and trade-offs, and rehearse your statistics fundamentals aloud.
Synthesized from 2 candidate reports by our editorial team.
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| Question | |
|---|---|
| 2nd Highest Salary | |
| Last Transaction | |
| Fair Coin | |
| Monthly Customer Report | |
| Cumulative Distribution | |
| Experiment Validity | |
| Cumulative Reset | |
| Always Excited Users | |
| Weighted Keys | |
| Total Spent on Products | |
| Reducing Error Margin | |
| Normal Distribution Sample | |
| Subscription Retention | |
| Size of Joins | |
| Hurdles In Data Projects | |
| Brain Cancer Treatment Outcomes | |
| Retailer Data Warehouse | |
| P-value to a Layman | |
| Detecting ECG Tachycardia Runs | |
| Justify a Neural Network | |
| Random Forest Explanation | |
| Causal Email Journey | |
| Merchant Acquisition | |
| Time Difference | |
| RMS Error | |
| Greatest Common Denominator | |
| Analyzing Churn Behavior | |
| Ranking Metrics | |
| Client Solution Pushback |
Synthesized from candidate reports. Individual experiences may vary.
The Data Scientist candidate reports submitting a CV and answering screening questions in a form, which gave Amgen an initial view of their background. The report describes it as straightforward. Keep your résumé consistent with your answers and be ready to expand on any project you list.
The same candidate was invited to an office a few weeks after applying. They describe five interviews and assessments scheduled one after another with little downtime, mixing technical and behavioral material. Expect a tiring day and plan your energy, since the pace is the main challenge reported.
One reported question asked the candidate to explain mean and standard deviation to someone without a statistics background. The candidate felt such questions test genuine understanding. Candidates may want to practice explaining core concepts intuitively, with simple examples, before the interview.
The Data Scientist candidate reports being asked for concrete examples from real work and for evidence they could communicate their thinking to technical and non-technical audiences. An intern candidate separately describes a behavioral round on teamwork, challenges, strengths and weaknesses. Prepare specific project stories with the reasoning behind your decisions.
A Data Science Intern reports a technical round using R and Python, with business-grounded questions on random forests versus decision trees and on approaching hypothetical datasets. This comes from one internship report, so full-time candidates may see different depth. It suggests being ready to explain model trade-offs in context.
