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Machine Learning Data Scientist Interview Questions

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80 machine learning data scientist interview questions shared by candidates

The online assessment has four sections: SQL Queries: Writing SQL queries. MCQ for Data Science: Multiple-choice questions related to data science. MCQ for Statistics & Probability: Multiple-choice questions on statistical concepts and probability. Python Coding: Writing Python code to solve a coding problem. The Technical Interview stage involves an interview with a Karat interviewer. This interview is not a typical discussion about the work you've done; rather, it's more like an extension of the online assessment. The interviewer asks questions similar to queries, poses statistics questions (e.g., about p-values and appropriate distributions for different scenarios), and requires you to solve a Python coding question. During this stage, you are allowed to use Google, but it's crucial to communicate openly about what you are looking up. Copy-pasting from sources like ChatGPT is discouraged, and the interview is conducted via video.
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Data Scientist/Machine Learning Engineering

Interviewed at Proxify

4.2★
Dec 11, 2023

The online assessment has four sections: SQL Queries: Writing SQL queries. MCQ for Data Science: Multiple-choice questions related to data science. MCQ for Statistics & Probability: Multiple-choice questions on statistical concepts and probability. Python Coding: Writing Python code to solve a coding problem. The Technical Interview stage involves an interview with a Karat interviewer. This interview is not a typical discussion about the work you've done; rather, it's more like an extension of the online assessment. The interviewer asks questions similar to queries, poses statistics questions (e.g., about p-values and appropriate distributions for different scenarios), and requires you to solve a Python coding question. During this stage, you are allowed to use Google, but it's crucial to communicate openly about what you are looking up. Copy-pasting from sources like ChatGPT is discouraged, and the interview is conducted via video.

ML: What is a difference between supervised and unsupervised learning? Give an example of both. How SVM algorithm works (general description)? How to make sure you are not overfitting while training a model? How you measure accuracy or the error rates? Spark: What is RDD? Why would you ever cache an RDD?
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Data Scientist Machine Learning

Interviewed at Pearson

3.5★
Oct 5, 2016

ML: What is a difference between supervised and unsupervised learning? Give an example of both. How SVM algorithm works (general description)? How to make sure you are not overfitting while training a model? How you measure accuracy or the error rates? Spark: What is RDD? Why would you ever cache an RDD?

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