One of the most interesting questions was on how to proceed with the release and analysis of a new feature of the product. It was an open question, given hypothetical information about a scenario, focused on thought process and problem solving.
Sr Data Scientist Interview Questions
3,390 sr data scientist interview questions shared by candidates
Background, and what I'm looking for in the next job
Data processing with sql on a live whiteboarding session
Why sometimes smaller models are better than bigger ones?
1) Technical difference between Pyspark and Pandas 2) If you have large dataset with 1000s of features and only want some important feature, how will you identify it? 3) Dimensionality reduction? And why PCA is not an ideal solution in real world? Any other options? 4) If you have to build a model for Business partners regarding calls received by them for Opening account, paying bill and q&a? How will you design the whole ML algorithm from scratch? What features you will consider as important? How will you get the data? All steps involved till end. 5) For continuous and categorical features, which ML algorithm is best? And why?
Given activity logs from the product, what features would you build to predict churn?
Explain what is target leakage.
How to generate a vector using word2vec?
Do you feel confident to lead projects?
Tell me about a time you’ve worked with a cross-functional group of people and had to convince them of something they weren’t originally convinced of.
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