1st round - Online test (coding + MCQs on data science) 2nd round - Technical interview. Questions related to your past work and projects. 3rd round - Technical interview. Questions on common ML algorithms, parameters, bias-variance, metrics, quant problems, statistics, probability etc.
1) How do you do cross-validation for a time series dataset? 2) What are the challenges compared to the usual k fold cross-validation?
Python related algorithms from leetcode. SQL coding ML concepts In depth analysis of the Machine Learning project I worked on
Q: can you please tell us a bit about your background? Q: How /Where did you get the labels for the NLP task?
Second is Technical HR: First question was about Filter the numbers in the list by exactly 2 digits and store it in a separate list. Second was prime numbers Projects which ive completed in data science python, works i have done in my previous organization. challenges ive faced in previous organization
Resume based
No detailed data science question will be asked they will ask very basic questions and rest is your luck. because you will not be able to know what and why did not like. the question such as how did you remove null value /outlier treatment/ training process.
Questions in Statistics, ML algorithms, Transformers, LLMs, etc. General questions about past projects.
Explain Transformers Explain BERT Model
SQL and Python pandas database transformation. Walk through a classification problem and caveats (how would you measure performance and feature importance)
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