Let's talk this time. Roche Data Scientist interview experience. Overall, I think the interview atmosphere at Roche is quite friendly, not like the "technical interrogation" of some companies, but more like a professional scientific discussion. The interviewer is very patient, and will guide your thoughts at the right time, and will also compliment you with "That's a good point", which makes you relax instantly.
Personally, I'm a research-oriented DS, focusing on clinical data and machine learning applications. The whole round lasted about 50 minutes and was divided into three parts: Coding Challenge, Machine Learning Discussion, and Domain-specific Questions, each of which is broken down in detail below.
Part 1: Coding Challenge
At the beginning of the interview, the interviewer handed out a clinical trial dataset containing fields for patient ID, treatment assignment, primary endpoint, and adverse events. The requirement was to analyze treatment efficacy and identify potential confounding factors.
As I was listening to the question, I realized that it was actually a combination of "statistical thinking + data analysis", not just pure coding.
I first took a quick look at the data structure and found that the primary endpoint is a continuous variable, so I used Python to first do the basic cleaning (missing value, outlier processing), and then wrote a simple groupby + aggregate to compute the difference in means for different treatment groups.
The interviewer asked me to explain the choice of statistical tests, "Why are you using t-test instead of other tests?"
I replied, "Because the primary endpoint looks continuous and approximately normal, and our sample size is reasonably large. So t-test would be If it were binary, I'd use a chi-square test instead."
He nodded at that and said "Good reasoning".
Then we moved on to the confounding factor discussion - this was the part I found most challenging. The interviewer asked, "How would you identify potential confounders?" I explained that you could start with a correlation matrix and look for variables that are significantly correlated with both treatment assignment and outcome. I explained that you could start with a correlation matrix and look for variables that are significantly correlated with both treatment assignment and outcome; then you could go further and use a regression model (such as multiple linear regression or logistic regression) to see if these variables actually confound the treatment effect.
The interviewer followed up with, "What if you suspect a hidden confounder that's not in the dataset?"
I replied, "Then I'd mention this limitation clearly in the analysis and, if possible, suggest using instrumental variable analysis or sensitivity analysis to estimate its potential impact."
This conversation is kinda like a dissertation defense, where it feels like the other person is examining the logic of thinking and the rigor of assumptions, rather than just the ability to code.
Part 2: Machine Learning Discussion
The second part was more conceptual and the interviewer asked an open-ended question, "If you were to design an ML system to predict drug-drug interactions (DDI), how would you approach it? "
I first defined the scope of the problem: DDI prediction is essentially a link prediction/classification problem, where the inputs can be molecular structural features (e.g. SMILES embedding, molecular fingerprints) and pharmacological features (target, pathway, indication, etc.) of a drug, and the output is whether there is an interaction between two drugs. The input can be molecular features (e.g., SMILES embedding, molecular fingerprints) and pharmacological features (target, pathway, indication, etc.), and the output is whether there is interaction between the two drugs.
The pipeline I propose is:
- Data level: construct positive and negative samples from publicly available databases (e.g. DrugBank, TWOSIDES);
- Feature level: using graph-based representations (e.g., extracting embedding from molecular graphs with GNN);
- Model level: try Siamese network or Graph Convolutional Network;
- Assessment metrics: AUC, Precision@k, and recall for unbalanced samples.
The interviewer followed up with, "How would you make the model interpretable?"
I mentioned the possibility of incorporating SHAP value or attention weight to help analyze which molecular fragments or mechanistic features lead to positive model predictions for positive interactions, which can improve interpretability in drug safety reviews.
The overall section was very much like a mini research proposal, and the interviewer was very interested and talked about the mobility of GNNs in the space of chemical molecules.
Part 3: Domain-Specific Questions
The last 15 minutes or so are field questions that focus on your understanding of pharmaceutical data and the clinical process.
Questions include:
- "What are the main phases of a clinical trial and what's the purpose of each?"
- "How would you handle missing data in clinical studies?"
- "Can you explain what a surrogate endpoint is?"
These questions may seem basic, but if you answer too textbook, the interviewer will immediately ask for details. That's why I try to incorporate practical examples in my answers, such as "In Phase II trials, we usually focus on efficacy and dose optimization, while Phase III aims for large-scale validation. That's why missing data handling here needs to preserve comparability across treatment arms."
The interviewer was satisfied with these kinds of answers and added some real challenges they see in Roche's internal pipeline, such as heterogeneity of multi-center trial data. you can feel that they are really on the front line of clinical AI related work.
Summarize
Roche's DS interview was one of the most relaxed and thought-provoking interviews I've ever had. The questions themselves were not "hard", but they were very thorough:
- Whether the statistical analysis is self-explanatory;
- Can you make scientific inferences in the context of domain knowledge;
- and whether the machine learning design reflects a research perspective and a sense of interpretability.
I don't think the key to performing well is whether or not you write perfect code, but rather being able to show the logic and curiosity of a scientist in the process.
The interviewer concluded by adding, "You clearly have both analytical rigor and curiosity - that's what we like to see."
It's really worth it to listen to it.
If you are also preparing for a DS position at Roche or a similar pharma company, it is recommended to be sure:
- Familiarize yourself with the structure of clinical trial data and common variables;
- Review statistical topics such as hypothesis testing, confirming, and missing data handling;
- Prepare a few more domain-aware ML cases, such as drug response prediction, biomarker discovery, or treatment effect estimation.
Overall experience, all I can say is - Roche's interviews are really scientific, friendly and test the depth of thinking.
Want to take a post like Roche Data Scientist? Don't go it alone!
Programhelp has long focused on data post coaching for North American technology companies and pharmaceutical companies (Roche, Pfizer, GSK, etc.), covering OA technical assessment, VO simulation, interview voice assistance and remote coaching.
Our lineup of assistant mentors includes a number of current DS/ML engineers from Roche, Novartis, and Amazon, who can work one-on-one with you to develop a strategy for your background, from clinical data analysis to machine learning system design.
Whether you're preparing for a Roche, IQVIA, or Biotech startup interview, we've got it covered:
Mock interviews + real-time voice reminders: Help you answer questions in a formal interview without getting stuck;
OA Remote and seamless assistance: ToDesk Secure Connection, test all passed;
If you want to take the Roche DS offer as easily as this student did.
You can contact us directly for an exclusive interview assistance program.
Get the next "Congratulations!" in your mailbox.
