Microsoft Interview Experience for Data Scientist

Round1: [45 Minutes]

First, the interviewer introduced himself and asked me to tell him about myself, domains I've worked on, projects, etc.

Coming to the interview, there were 3 case studies:

1. Given a set of features and actual label as an ordinal feature(0-4), we pass it to the binary black-box classifier that will provide the probability of y=1, i.e., P(yi=1). The task is to predict ordinal value(0-4) using the probability of the above black-box classifier.
2. Given paraphrased sentences, the task is to find whether sentences are paraphrases or not? How do you come up with features like feature engineering techniques? Which deep learning model would you prefer in this case and why?
3. Given a set of unlabeled images stored somewhere, the user will provide queries in the form of colors like if a user writes red, need to retrieve corresponding images? Colors can be treated as a categorical feature(at most 25 colors for this problem). How to scale it or optimize the search engine to retrieve images efficiently?

Tips: Keep interacting with the interviewer so that he can know in which direction you're thinking.

Round2: [40 Minutes]

First, the interviewer introduced himself and asked me to tell him about myself, domains I've worked on, projects, etc. It was a chill experience.

Some of the questions that he asked me about:

Thesis work: scalability, how data is stored in the database, how can it be improved? How does latent semantic indexing work? He asked extensively about my thesis.

Questions related to my recent NLP project: "FastAttack".

Some of the general questions related to my other self projects.

The most challenging code that you've ever written?

What are the problems that can be solved using Dynamic programming?

In which data structures you're confident? Name some of the problems.

I was not interviewed for further rounds, not sure about the reason.

Verdict: Rejected.