Github: Difference between Merge and Rebase
Lead Data Scientist Interviews
Lead Data Scientist Interview Questions
"As a lead data scientist, you will be responsible for developing creative and effective improvements to a company's product by analyzing data from consumers, websites, sales, and many other sources. During an interview, expect to be given many case studies about what information you can draw from a particular set of data as well as to be tested on your general statistical and computer science knowledge. A background in computer science, statistics, or another related field is required. "
398 lead data scientist interview questions shared by candidates
Python: what is context manager? What are decorators?
Lots of SQL work and light python work. No major questions about machine learning. I think that that's where they want to be soon, but they're just not there yet.
Pricing and Market entry questions.
Describe a good impactful project
Have you deployed models on cloud
Two first-round technical data science interviews for two different roles within CVS Health. Both interviewers were very nice to speak with; however, their approach—and consequently, my impression—was completely different. 1. SQL Interview (should have been easy, right?) – Terrible experience! Three or four well-defined tasks were given. However, the partially written SQL queries and the lack of direction on whether I was required to modify the existing code or implement my own solution left me scrambling to debug the poorly written queries before even proceeding, as the original queries failed to run from the start. Oh, and the interviewer simply dropped the code into CoderPad, went on mute, and seemingly did other things, making it impossible to ask questions. 2. Python Interview – Excellent experience with the interviewer(kind & professional). The question was moderately challenging (LeetCode medium or a simplified hard), and although I wasn’t able to fully complete the solution in time, I was given a helpful hint along the way.
Typical behavioral and deep dive in technical projects with questions focused on ML design.
Tell me a situation when you cant not deliver the result on time and how to deal with it.
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