Bristol-Myers Squibb Data Scientist interview typically runs 3 rounds: hiring manager chat, HR call, final presentation and roundtables. It usually takes about 3 weeks and is notably presentation-heavy and cross-functional.
$160K
Avg. Base Comp
$220K
Avg. Total Comp
3
Typical Rounds
3 weeks
Process Length
Our candidates report that Bristol-Myers Squibb cares less about flashy algorithm talk and more about whether you can defend your work in a scientific, business-aware way. The strongest signal in the experience we saw was the presentation: it wasn’t just a recap of past projects, but a test of how well the candidate could explain the research question, the choices behind the approach, and the results in a way that held up under scrutiny. That tells us the bar is not simply technical competence; it’s whether you can make your thinking legible to people who may come from different functions.
A recurring theme is the company’s interest in drug-development context. The follow-up discussion went beyond the candidate’s own project into broader industry judgment, including what matters most in drug development and how a molecule would be synthesized. We’ve also seen that they pay attention to influence and collaboration: one behavioral prompt asked about persuading someone to accept an idea, which suggests they value scientists who can move work forward across teams, not just analyze data in isolation. In practice, the candidates who seem to do best here are the ones who can connect technical decisions to scientific tradeoffs and explain why those choices matter to the organization.
Synthetized from 1 candidates reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Bristol-Myers Squibb
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Cumulative Distribution | |
| Last Transaction | |
| Always Excited Users | |
| Total Spent on Products | |
| Brain Cancer Treatment Outcomes | |
| P-value to a Layman | |
| RMS Error | |
| Reducing Error Margin | |
| Cumulative Reset | |
| Hurdles In Data Projects | |
| Time Difference | |
| Impute Median | |
| Fair Coin | |
| Random Forest Explanation | |
| Greatest Common Denominator | |
| Subscription Retention | |
| Secret Wins | |
| Missing Housing Data | |
| Sum to Zero | |
| Valid Anagram | |
| Licensing Valuation | |
| Rider Discount | |
| Second Longest Flight | |
| Digit Accumulator | |
| Search Linked List | |
| Multi-Reaction | |
| Common Prefix | |
| Count Transactions | |
| Data Preparation for Imbalanced Data |
Synthesized from candidate reports. Individual experiences may vary.
The process began with a conversation with the hiring manager and two other team members. This felt more like an introduction than a deep technical screen, with questions focused on the candidate’s background, motivation for the role, and how they would describe their prior work.
Next was an HR call covering basic background and logistics. The discussion included salary expectations and visa-related questions, along with other standard administrative topics.
The final stage centered on a one-hour presentation of prior work, followed by two roundtable interviews with three to four people each. These discussions were much more technical and cross-functional, covering the candidate’s research topic, methods, results, industry knowledge, drug development considerations, and behavioral questions about influencing others.