Starting 20-25 min: They directly started with questions from my resume without introduction mainly focusing on NLP and Machine Learning concepts

NLP & Tokens: What are tokens in NLP Types of tokenization? Why token count matters in models ?

Steps involved in preprocessing text Stop word removal, punctuation cleaning, lemmatization Why preprocessing is required

What is TF-IDF How text is converted into numerical form

Ml models : Logistic Regression SVM Why these models are used for text classification

Evaluation Metrics: Accuracy Precision Recall F1-score When to use precision vs recall Confusion matrix basics (TP, FP, TN, FN) SVM Concepts What is margin What are support vectors Why maximizing margin is important

Then he asked me do u have any idea on llms I said basic understanding

Then ml coding - 30 min

Ml coding question : Finding minima of a function Iterative updates using derivatives

Last me he asked me do u have any questions for me

In Round 2, the interviewer was a senior engineer. The round started with a brief introduction about myself. After that, I was asked to choose any one project that I had strong knowledge of.

I decided to discuss my Data Science internship project. The interviewer then spent around 30 minutes diving deep into the project. The discussion was very detailed and focused heavily on understanding my concepts, decisions, and implementation.

The interviewer kept asking follow-up questions on my answers and wasn’t easily satisfied. In fact, he repeated the same questions multiple times, expecting more precise and improved responses each time. The level of depth and cross-questioning made the round quite challenging.