Skip to contentSkip to footer

Sr Data Scientist Interview Questions

Salaries
Interviews

3,380 sr data scientist interview questions shared by candidates

difference between regression and classification then what is difference between linear and logistic if logitistic regression is classifying why is it called regression evaluation metrics for linear regression. difference between rsquare and adjusted rsquare? what if an irrelevant feature is kept will it increase rquare or will it stay the same? what does rsquare tells you? how does rsquare works or how does it behaves? if the rsquare is 0.8 what does it mean? what are we interpreting from 0.8 which of the l1 and l2 is direction based? what are the advantages and disdadvantages of kmeans clustering? what are the methods to determine optimum k? what does silhouette score mean how it works? why it is called elbow method? how are you going to pass categorical feature to kmeans clustering? if you have 100 features are you going to pass it directly or what will you do? consider two tables a and b, table a has 10 rows, table b has 20 rows. both have ID column to match on. If I do the inner join what is the minimum and maximum number of rows that we will get? select 3 from employee - what will this print [{}({)}] how to balance the brackets using python? what in this case - [{}()]?
avatar

Senior Data Scientist

Interviewed at Tredence

3.9★
Oct 8, 2024

difference between regression and classification then what is difference between linear and logistic if logitistic regression is classifying why is it called regression evaluation metrics for linear regression. difference between rsquare and adjusted rsquare? what if an irrelevant feature is kept will it increase rquare or will it stay the same? what does rsquare tells you? how does rsquare works or how does it behaves? if the rsquare is 0.8 what does it mean? what are we interpreting from 0.8 which of the l1 and l2 is direction based? what are the advantages and disdadvantages of kmeans clustering? what are the methods to determine optimum k? what does silhouette score mean how it works? why it is called elbow method? how are you going to pass categorical feature to kmeans clustering? if you have 100 features are you going to pass it directly or what will you do? consider two tables a and b, table a has 10 rows, table b has 20 rows. both have ID column to match on. If I do the inner join what is the minimum and maximum number of rows that we will get? select 3 from employee - what will this print [{}({)}] how to balance the brackets using python? what in this case - [{}()]?

The first interview involved general data science questions that any average data scientist could easily answer. The data science questions in the second interview were also easy. The data scientist interviewing me from Sweden didn’t seem to know much about the field. It felt like a student interviewing someone at a professor level. However, I'll give their knowledge graph expert respect, as they were questioning my skills in this area well. The questions about knowledge graphs were about concepts on ontologies, OWL, and SHACL, which I had good experience with since they are part of my PhD thesis. The third interview questions were more about hiring, onboarding, salary, and a bit about my technical past experiences from the hiring manager. In the additional fourth interview, the lead in Oslo mentioned that he wasn't very good at coding. I thought he might be just being humble. He also said he came from a biology background, so I mentioned that was my weak spot and that I was learning to code genetic algorithms. He then admitted he didn’t know about that either. Unfortunately, this seems to be their level of expertise. His questions were just situational questions and cliche questions in management.
avatar

Senior Data Scientist

Interviewed at Capgemini

4.2★
Jul 3, 2024

The first interview involved general data science questions that any average data scientist could easily answer. The data science questions in the second interview were also easy. The data scientist interviewing me from Sweden didn’t seem to know much about the field. It felt like a student interviewing someone at a professor level. However, I'll give their knowledge graph expert respect, as they were questioning my skills in this area well. The questions about knowledge graphs were about concepts on ontologies, OWL, and SHACL, which I had good experience with since they are part of my PhD thesis. The third interview questions were more about hiring, onboarding, salary, and a bit about my technical past experiences from the hiring manager. In the additional fourth interview, the lead in Oslo mentioned that he wasn't very good at coding. I thought he might be just being humble. He also said he came from a biology background, so I mentioned that was my weak spot and that I was learning to code genetic algorithms. He then admitted he didn’t know about that either. Unfortunately, this seems to be their level of expertise. His questions were just situational questions and cliche questions in management.

Need to classify (images or texts) into 4 classes. If we only have 1000 labeled samples, how can we use an LLM to get millions of labeled samples? How to do clustering? How to determine whether the resulting clusters are good?
avatar

Senior Data Scientist

Interviewed at Gong

4.4★
May 25, 2026

Need to classify (images or texts) into 4 classes. If we only have 1000 labeled samples, how can we use an LLM to get millions of labeled samples? How to do clustering? How to determine whether the resulting clusters are good?

The business case in the technical interview was the most important part. The scenario had two marketing campaigns running at the same time, and I had to explain how to measure their combined value and decide whether to keep campaign A, campaign B, or both together.
avatar

Senior Data Scientist

Interviewed at Booking.com

4★
Jun 16, 2026

The business case in the technical interview was the most important part. The scenario had two marketing campaigns running at the same time, and I had to explain how to measure their combined value and decide whether to keep campaign A, campaign B, or both together.

Viewing 1601 - 1610 interview questions

Glassdoor has 3,380 interview questions and reports from Sr data scientist interviews. Prepare for your interview. Get hired. Love your job.