Autoencoders, CNN, RandomForest, Price optimization, Linear programming, Regression, Epidemiological models(based on my resume), generic questions about some of the key ML concepts like logistic regression, feature engineering and hyperparameter tuning.
There were bunch of puzzles that were asked, some real life questions and some about your resume. Fairly decent process and the factualty is very helpful, they don't make you stress in the interview.
normal questions from the project case study
Design a system for presenting data insights to the users
1. Programming Question on Stock Maximization. 2. Data Modelling questions 3. SQL 4. Questions on general ML and deep dive. (Prepare this well)
explain a/b testing, some probability questions.
Describe and implement bootstrap sampling
You've built an ML model and put it into production but now you find that the performance is dropping. What steps would you take to investigate and mitigate the issue?
Which programing languages I like to use? Why do I want to switch to industry from academia? Which part of a data science framework I like the most, which the least?
mostly about statistics,python logicsl programming, sql
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