Name: Kaustav Prasad
Profile: Data Science Intern
Company: Microsoft

What was the prep you did before the interview, and for how long?:
Had been doing competitive programming and data analysis and ML projects since freshie year; regularly practised DSA and aptitude tests from 2 months before interview

How long was the interview for each round?:
1hour each (2 interviews)

What significant things were looked upon in your resume in the interview?:
ML Projects (asked to select a project from my resume that I learnt a lot from and would like to talk about)

What other questions were asked in your interview?:
DSA questions that I had to solve and code up on the spot (difficulty level was close to Leetcode medium problems), HR questions related to why i want to do data science/ML, what interests me about it, etc

Describe your typical day at work in brief:
Start working (anytime between 9 and 11 AM is ideal; flexible hours), make progress on the project you're working on, try new ideas, learn a technology you're unfamiliar with if it it's needed to make progress, have lunch and take a break, work again till around 5:30 and either leave by 6, or unwind via the music room, Xbox room etc in the evening, then have dinner in the office and leave. In case you have to complete more work than usual, work from the evening to dinner time instead of unwinding and leave after having dinner (usually in the office).

What difficulties did you face during the early stage of your internship?:
As I was working in the Enterprise Commerce subdivision, understanding how billing, invoices, pricelists, resource allocation etc work was pretty challenging, and reading the company documents on this wasn't enough to get an understanding of the data and a sense what's going on; I only gained this understanding by sitting down with a team member and having a back-and-forth discussion on these topics.

What was the most crucial thing which you learned or any skills that you acquired during the internship?:
Working on a project of significance and then convincing others that it indeed is of significance and why. In terms of skills, I gained the following: writing industry standard code and adhering to industry standard software and ML development workflows and practices, writing end-to-end ML pipelines from scratch, domain knowledge in cloud computing services, and people skills - especially when it comes to getting your point across to upper management.