The panel had conducted two interview rounds of which the first one was a hands-on technical round and the second one was a behavioral one. Round-1 (Hands-on technical round) They had asked me few behavioural questions like what draws me towards data science, my skills, my best project, dealing with bugs and errors, dealing with disagreement within team, handling pressure, meeting deadlines, etc. Later, I was given a toy dataset and I had to perform data science operations over it using Python or R. I had used Pandas. The next section asked me to write SQL queries for retrieving data from another toy dataset. I had been given 45, minutes in total for completing these tasks. Round-2 (Behavioural round) I found this round pretty much relaxed as compared to the first one. It was a meeting with senior managers and they had asked me questions based on my research papers, machine learning metrics, explaining neural nets to a layman, dealing with humongous datasets, Occam's razor, etc. They had also asked questions based on my GitHub projects. In the end, there were some light weight questions like how do I like Canada, how do I deal with snow here, etc. It was a positive experience. I had enjoyed to my best during these rounds. Thank you!

The interview process is generally straightforward and consists of two main stages: 1. HR Round This stage is relatively light and mainly focuses on self-introduction, reviewing past experiences, and clarifying details on your resume. HR will pay attention to communication skills, motivation for applying, and overall cultural fit with the company. The questions are usually basic and not very technical. 2. Team Interview (Behavioral + Technical) This round is conducted by potential team members and combines both behavioral and technical assessments: Behavioral Questions (BQ): These are standard situational and experience-based questions such as self-introduction, past project involvement, teamwork experiences, and problem-solving approaches. Technical Questions: The technical portion usually covers fundamental skills in Python, SQL, and SAS. The level of difficulty is moderate, focusing more on practical applications rather than overly complex or advanced algorithms. Overall, the process is not difficult if you are prepared with your resume highlights, basic behavioral answers, and solid foundational knowledge in the technical areas.