Roche Data Scientist Technical Interview Review｜Real Scenario Explanation + Interview Idea Sharing

talk about Roche(Roche), the first thing that comes to the mind of many students is its strength in pharmaceutical research and development. As a global leading biopharmaceutical company, Roche's investment in AI for Drug Discovery and Precision Medicine has been quite aggressive over the past few years, so the recruitment of Data Scientist has been a long time favorite, especially in favor of talents with cross backgrounds who understand Healthcare business.

This is a video recording of programhelp assisting a student to remotely prepare for a Roche DS interview, we assisted the student to go through the whole process from coding details, domain knowledge, to ML system design, to make sure that he can not only write code, but also reasoning. this round of interviews is not a low level of technical content, and it is very suitable for those who prepare for the This round of interview is very technical and suitable for those who are preparing for DS positions in pharma / biotech direction.

Overview of interview content (Technical Round)

The interview was divided into three components:

Part I: Real-world clinical trial data analysis

The interviewer directly dumped a simplified version of the clinical trial data for me to read, the core variables are treatment group, primary endpoint, response rate, adverse events, some patients' baseline characteristics. I took one look at the data and realized that the question was really about--

"Can you tell if a particular drug is working or not based on data?"

So my first reaction was to run the means and standard deviations for the subgroups and then use a t-test to test the difference between treatment vs control.

The tricky part is that the interviewer will ask you why you chose this test and whether you considered confounders, but luckily I've encountered similar things in healthcare data projects and know that it depends on whether the baseline is balanced or not, so I took it right away:

"In addition to looking directly at the endpoint differences, I would also go ahead and compare the distribution of the two treatment groups on the variables of AGE, BASELINE SERIOUSNESS, and COMBIDENESS to make sure that there were no systematic differences between the groups."

Then I wrote a piece of Python code to do a t-test or chi-square on each of these variables to see the balance.

This piece I prepared when Programhelp helped me sort out the caliber, for example:

• What is the test for continuous vs categorical variables?
• When do we do propensity score matching?

This kind of detail is easy to be challenged by the interviewer if you have not usually stepped on the pit.

Part II: Machine Learning Design Questions

The interviewer in this round came up with interesting questions, favoring system design:

"If you were to make a model to predict drug interactions, how would you design it?"

The first thought that came to my mind was the classification model, but to reflect the structural understanding, I talked about modeling it with a GNN (graph neural network) because drugs are graph structures by nature, and the combination of atoms + bonds is a natural graph, and a GNN captures the topology, which is much more expressive than a fingerprint.

I went through the four steps along the lines taught by Programhelp:

1. Data level: Training data will be constructed from pharmacopoeias, literature databases, and known interaction records.
2. feature engineering: Includes molecular structure, metabolic pathway, targets, etc.
3. Model Selection: GNN + some ensemble methods for comparison baseline.
4. Assessment methods: Slicing training tests with a time sliding window to avoid information leakage and mentioning the need for external validation.

At that time, the interviewer nodded his head a lot and said, "You are quite clear about the GNN structure, and the VALIDATION STRATEGY is well thought out."

Part III: Pharma domain knowledge quiz

This part was more of a chat, with the interviewer asking a couple of quick-answer style questions:

• "How do you see the difference between real-world data and clinical trial data?"
• "What does the FDA know about the specification of ML models?"
• "How do you deal with missing medical data in your usual programs?"

I'm not the kind of data scientist who specializes in drug approvals, but Programhelp has helped me organize some basic calibers in simulation sessions, for example:

"RWD may be more representative, but the noise is also large and requires a more robust approach; the FDA currently has a guidance document specifically for AI/ML, and while I haven't read it all, I know that special attention should be paid to reproducibility and explainability. "

This part doesn't necessarily test whether you can do it or not, but rather how well you can convey your sensitivity to the industry in a conversational way.

Programhelp's assistance experience

My biggest feeling during this preparation is that the interview of Data Scientist in the pharmaceutical field is not about "problem solving ability", but about whether you can tell a credible story with data.

Instead of memorizing templates, Programhelp helped me prepare by online coding and voice-assisted methods, adjusting the logic while speaking, and training me how to break down a healthcare problem, analyze it, and explain why I did it in less than 30 minutes. I think this is especially worthwhile, especially for students who don't usually do healthcare projects, it's a huge acceleration.

Summary of recommendations

Roche's interview style is actually quite "scientist" style, the pursuit of logical clarity, explanation of the full, not so critical of the code, but very concerned about reasoning:

Read more clinical data analysis papers before the interview, especially treatment effect estimation related;

Proficiency in basic statistical thinking such as t-test, chi-square, confounder analysis;

Prepare plenty of industry jargon, even if you're not a pharmacy major, knowing some regulatory trends will be a plus.

If you are also preparing for Roche / Pfizer / BMS / Merck etc pharma DS post, it is recommended to find professional coaching earlier to go through the systematic interview ideas together, otherwise you may not even be able to read the questions clearly.

That's what I'm looking for. programhelpThe interview coaching program of pharma & bioinformatics is specialized in pharma & bioinformatics interview coaching, which can not only help you brush up the questions, but also on-line to accompany you to push the idea and talk about the background, which really saved me a lot of effort.

azn7u2@gmail.com

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