Tiger Analytics Data Scientist Interview Questions
& Process

The interview process included two technical rounds. The first round covered fundamental Machine Learning concepts like linear regression assumptions, bias-variance tradeoff, and evaluation metrics (Accuracy, Precision, Recall, Sensitivity, Specificity). There were also Python coding questions on sorting lists, traversing arrays, and counting duplicates into a dictionary. SQL questions, including those with window functions, and project-based questions from the resume were also part of this round. The second technical round involved a detailed discussion about one of the projects and Python coding exercises focused on mean imputation and data manipulation with pandas.

The technical assessment and interviews were tough but relevant to the data scientist job. However, after I cleared all rounds and was told I got the job, they told me about a hiring freeze for my position. They then offered me a different job with a lot less pay than we talked about, which felt wrong because of the expectations. I found out other people had the same thing happen, which makes me question their honesty and ethics. It feels like they might be using the interviews to get info on people's projects without really intending to hire. To management: Please make your hiring process more honest and respectful of people's time and effort.

The process includes 6 rounds: resume screening, an assessment test, and then 4 interviews followed by HR. I made it to the 2nd interview, where they asked about everything, including detailed questions on algorithms, statistics, and formulas. The 3rd round was supposed to be about use cases, but the interviewer, Rajesh, asked 3 questions. I only managed to answer one question fully and another one halfway, and that's why I was rejected. So, if you're interviewing with TIGER, be prepared to answer every question without fail.