Data Science Consultant Interview Questions

40,327 data science consultant interview questions shared by candidates

The technical interview required live coding. I don't recall all the details, some combinatorial problem that probably should've been easy but it's been a while since I've coded and was a bit caught off-guard by having to write code when interviewing for this job, which by all accounts would not involve individual technical contribution.
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Data Science Manager

Interviewed at Flatiron Health

3.2
Feb 2, 2021

The technical interview required live coding. I don't recall all the details, some combinatorial problem that probably should've been easy but it's been a while since I've coded and was a bit caught off-guard by having to write code when interviewing for this job, which by all accounts would not involve individual technical contribution.

Where does Deep Learning offer advantage compared to SVMs? Is the cost function of a DNN model convex? What about for SVM? Tell me about how you have implemented a research paper (mentioned in my resume) Basic questions about linear and logistic regressions - about their assumptions, advantages etc Overall, the questions weren't too deep.
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Senior Data Scientist

Interviewed at Ericsson

4
Oct 14, 2018

Where does Deep Learning offer advantage compared to SVMs? Is the cost function of a DNN model convex? What about for SVM? Tell me about how you have implemented a research paper (mentioned in my resume) Basic questions about linear and logistic regressions - about their assumptions, advantages etc Overall, the questions weren't too deep.

1. What's the relationship between PCA and k-means clustering? 2. What are the requirements for a matrix to represent a kernel? What happens if we run SVM using a 'kernel' that does not satisfy these requirements? 3. Problems using Python lists and dictionaries 4. SQL joins, aggregates (count, sum, avg), and cases 5. If you were given a dataset with [X] features (may be numerical, categorial, etc.) and you want to build a model (to determine fraudulent transactions, say), how would you determine which features are best to use in the model?
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Data Scientist

Interviewed at Palo Alto Networks

3.7
Apr 27, 2019

1. What's the relationship between PCA and k-means clustering? 2. What are the requirements for a matrix to represent a kernel? What happens if we run SVM using a 'kernel' that does not satisfy these requirements? 3. Problems using Python lists and dictionaries 4. SQL joins, aggregates (count, sum, avg), and cases 5. If you were given a dataset with [X] features (may be numerical, categorial, etc.) and you want to build a model (to determine fraudulent transactions, say), how would you determine which features are best to use in the model?

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