Senior Scientist Interview Questions

5,956 senior scientist interview questions shared by candidates

Questions around projects done in earlier workplace. Explanation of some core deep learning concepts and it's application. Discussion on details of model and data science application experienced during earlier projects.
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Senior Data Scientist

Interviewed at Fractal

4.1
Nov 2, 2020

Questions around projects done in earlier workplace. Explanation of some core deep learning concepts and it's application. Discussion on details of model and data science application experienced during earlier projects.

The technical screening involved some leetcode style problems and general problem solving + ML questions. The round table was a mix of behavioral questions, statistics, ML, and at least 1 more instance of leetcode problems. Question difficulties ranged from easy, like "how to address in the dataset", to average difficulty questions like "explain the differences between and another" or "can you describe how works.
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Senior Applied Scientist

Interviewed at iHerb

3.8
Aug 12, 2023

The technical screening involved some leetcode style problems and general problem solving + ML questions. The round table was a mix of behavioral questions, statistics, ML, and at least 1 more instance of leetcode problems. Question difficulties ranged from easy, like "how to address in the dataset", to average difficulty questions like "explain the differences between and another" or "can you describe how works.

1. write a python code foo(x) where foo(4) =3 and foo(3)=4 2. How would you design a google calendar-like app. 3. Given a jupyter notebook, find what was done wrong. Try going over model assumptions, accurate metrics for imbalanced data etc... They give you the data in advance so try going over it and predict what they might ask about it
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Senior Data Scientist

Interviewed at Pendo

3
May 9, 2023

1. write a python code foo(x) where foo(4) =3 and foo(3)=4 2. How would you design a google calendar-like app. 3. Given a jupyter notebook, find what was done wrong. Try going over model assumptions, accurate metrics for imbalanced data etc... They give you the data in advance so try going over it and predict what they might ask about it

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