there was two round of interview , both were technical rounds. they asked about my projects , some coding related questions and also asked some data structure and algorithms concept
I applied in-person. The process took 3 days. I interviewed at Draup (Bengaluru)
Interview
Was called for the interview on the phone, and a online f2f interview was scheduled the next day.
The interviewer started the interview late. Had to cancel my future meetings for this interview!
The interviewer was mostly consisted of DSA questions. Solved it in a breeze. But apparently the interviewer wanted me to solve the questions in his way only. But then again, easily cracked his further questions.
Later it was time for me to ask questions, asked him about the role in the company. Guess what kind of a role i would be expecting from the interview where he asked all these complicated DSA questions.
An amazing web scraper job!
was pumped up about the web scraping job where i can apply all my hard earned DSA skills which i learnt. And the pay of course is the same as a data engineer job.
If the teammates your working with are so arrogant in the interview, then during the job, it's gonna be worst.
Avoid at all costs. Pay is garbage anyways.
Interview questions [1]
Question 1
What is the difference between numpy and list datastructure?
I applied through university. The process took 1 day. I interviewed at Draup (Bengaluru) in Jan 2021
Interview
One online test of technical questions related to ds and algorithms and codes. 2 coding questions, one of palindromic prime and 2nd on lexicographical strings. Two rounds of technical interviews. Later on hr round.
Interview questions [1]
Question 1
Questions regarding machine learning, accuracy, precision, recall, f1 score were asked in the first round of interview. questions on nlp, gpt2, transformer model etc. It was for 35min.
2nd round interview was for 45-50min. Questions asked on machine learning algorithms, svm, knn, random forests, feature engineering, cnn, lstms, coding questions on bst and linked lists. Real life problems of data science. It is mostly related to resume.