Candidates for Roche's Data Scientist role describe a mix of video screens, manager and technical conversations, and behavioral final stages, with timelines of roughly two to five months and mixed outcomes.
$168K
Avg. Base Comp
$221K
Avg. Total Comp
Not reported
Typical Rounds
2-5 months
Process Length
Reports from candidates who interviewed for the Data Scientist role describe a process that leans on behavioral and communication questions, with technical content that depends on the team. Of six such reports, two ended in offers and four did not.
Two candidates started with a recorded video screen. One describes a HireVue-style round on collaboration, conflict resolution and working across cultures, plus a question about where they see themselves in five years. Another describes a one-way video round combined with a coding assessment on SDTM and ADaM clinical datasets. Others began with a call with a team leader, team manager or hiring manager, or with an online assessment.
Technical depth varies. One candidate had a live-coding round with a senior data scientist and questions on handling large datasets with many variables. Another faced SQL and general data science questions about dataset processing and tools. A third described a video interview focused on their specific area of expertise. Final stages were often a panel or manager conversation about fit, motivation and understanding of Roche, and one candidate also gave a team presentation.
Timing is the main frustration. Candidates report anywhere from about two to five months, with long waits between steps and, in two accounts, little or no communication at the end. One candidate heard back with an offer about a week after their last interview.
Prepare STAR stories about your biggest projects, a concise answer on why Roche, and the techniques on your CV. If the role is clinical, review SDTM and ADaM, and keep SQL sharp.
Synthesized from 9 candidate reports by our editorial team.
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| Question | |
|---|---|
| Missing Housing Data | |
| Addressing Data Quality Issues | |
| Testing Constraints | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Marketing Workflow Optimization | |
| Decreasing Tech Debt | |
| 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 | |
| Retailer Data Warehouse | |
| P-value to a Layman | |
| Detecting ECG Tachycardia Runs | |
| Justify a Neural Network | |
| Causal Email Journey | |
| Merchant Acquisition | |
| Time Difference |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates describe a recorded video round early on. One reports behavioral questions on collaboration, leadership, conflict resolution and cross-cultural teamwork, plus a five-year question. Another reports behavioral questions alongside a coding assessment on SDTM and ADaM datasets. One candidate waited close to two months for this step.
Reports describe a CV and project walk-through with a team manager, an hour-long call with a hiring manager and talent acquisition partner, and a technical round. Topics reported include SQL, data science fundamentals, dataset processing, live coding with a senior data scientist, and handling large datasets with many variables. One candidate described a video interview centered on their own area of expertise.
Final stages reported include a mostly behavioral team panel that asked about Roche, a hiring manager round on why Roche and specific techniques, a team presentation, and a senior manager conversation with a five-year question. One candidate mentions an assessment centre. One heard about an offer a week after the final interview, while two others reported no clear follow-up after their last round.
