The interview was well organized and based on fundamental concepts. If you know your stuff, it will be easy. The interview team was well organized and finished it on time.
Interview was of 3 rounds.
1. Call with Recruiter.
2. Call with Data Science manager - 30 min
3. 90 Minute call with Panel - Behavioural questions for 30 min, Technical questions for 30 min, and 30 minute Python coding questions
Interview questions [1]
Question 1
- Tell me about a time where you have worked with group?
- Tell me about a time where you took some key decisions which impacted larger group?
Technical questions:
- What is the difference between Mean Absolute Error and R- Square? Tell me which metric to depending on use-case?
- How do you detect outliers?
- What is the mathematical formulae for R-square?
- What do we call/tell when model performs better on training data and not on test data?
-Which are different types of evaluation metrics? What is F1 score?
-Which metric is good to use with imbalanced data? What is Precision?
-What is data leakage?
- What do you when there are null or missing values in dataset?
Python Interview:
- What is difference between List, Tuple, Array?
-What is difference between Pandas series and Dataframe?
- What is join and merge?
- What is pickling?
- What are different types of python libraries?
- Name one dataset with imbalanced data? How do you go ahead with model building? What are pros and cons?
** There were few python questions on strings, lambda functions, date functions.
