Stated on the descriptions above.
Data Scientist Machine Learning Interview Questions
131 data scientist machine learning interview questions shared by candidates
Data science related questions Project related questions
what is your experience with machine learning
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.
- what would your colleagues say about you? - Talk about a project/work you're most proud of.
Classic interview questions. On prem interview which is fairly hard.
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?
I couldn't share the interview questions.
Given a model that gives the likelihood that a loan is fraudulent, how should you proceed?
How would you deal with unbalanced data? Which are the assumptions of the linear regression?
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