Tiger Analytics Data Analyst Interview Questions
& Process

First, we'll look at your resume to see if you have the skills we need, like SQL, Python, Tableau, and Power BI, and if you know the analytics stuff. Then, HR will chat with you to get a feel for your background, skills, and if you're into the job. They'll ask about your experience, what you want to do, when you can start, and what you're looking to make. After that, there's a technical part where we check your skills. This usually means SQL questions about making fancy queries, moving data, and joining tables. Also Python stuff like cleaning data, analyzing it, and using Pandas or NumPy. We'll also throw in some logic and reasoning problems. Sometimes this part could be a live coding test or an online challenge on HackerRank or Coderbyte.

So the interview process for this senior data analyst role started with an online test that covered finding the p value, z score, and some general data science concepts, along with python code snippets. It also included 2 python coding problems, focusing on easy LeetCode style questions for strings and lists. No SQL was involved in this round. Then came Round 1, where we talked about my recent projects, and they asked about every single line of code in those projects. This round also had 2 live coding problems, again on strings and lists. Round 2 was with a panel and was pretty disappointing, with really tough questions on logistic and linear regression, including the math behind them.

The process started with a 40-minute written test, which had two sections. The first had 20 aptitude MCQs, and the second had 10 coding MCQs about input/output and basic programming. The difficulty was moderate. After that, 108 people were shortlisted. Having an ML project on your resume gives you an edge. The interview itself was on Day 3, Slot 1. The first round focused on programming skills and problem-solving. It began with resume questions and a 'tell me about yourself' prompt. Then, questions about my internship experiences and competitor analysis came up, followed by questions on my ML project. I was asked to write a Python program for KNN, but I mentioned using the sklearn library, which led to a debate about coding versus understanding libraries. The interviewer then asked for pseudocode for finding the shortest distance between two words in an array and its optimal approach. Finally, I was asked to explain Random Forest but explained Decision Tree instead, which ended the round on a light note. This round lasted about 40-45 minutes, and I was sent to the next round. The second round, lasting about 45 minutes, assessed analytical and communication skills. The interviewer was the VP of Data Science. It also started with resume questions and 'tell me about yourself.' Questions covered my views on data's impact, my startup, key eCommerce matrices, calculating the Indian grocery market size, and some stats/data cleaning questions. I was also asked if I had any questions for them.