ZS

Role: Data Science Associate

College: Not Specified

Interview Date: Applied March 2020, Rounds in mid to late 2020 (Inferred)

Interview Type: Full-Time

Result: Selected

Difficulty: Medium to Hard

Rounds: 3 rounds

Topics Asked: Machine Learning (problem-solving, model building, classification, NLP, Deep Learning, CNNs, statistics), Case Debrief (presentation of solution, approach, results, source code), Projects, Resume, Strong areas in ML.

Experience:

The candidate applied for the Data Science Associate (DSA) role in March 2020. The interview process consisted of 3 elimination rounds.

Round 1 (Machine Learning Challenge): The first round required solving a Machine Learning problem and submitting the predictions in a CSV file along with the source code. An example problem involved classifying "Job Type" into 6 classes and "Job Category" into 11 classes.

Round 2 (Case Debrief): After submitting the Round 1 problem, the candidate received a call from HR for the second round, which was a technical discussion of the ML challenge and the candidate's solution. The candidate had to create a PowerPoint presentation describing the steps taken, the results obtained, and the source code. The interview was scheduled on Zoom for 1 hour, requiring screen sharing to present the solution. After the presentation, the interviewers asked detailed questions about the approach from the very first step.

Round 3 (Technical + Fit round): This was the third and final round. The candidate initially expected questions on their resume, projects, and skills. However, the interviewer had different plans, starting by asking about the candidate's strong areas in Machine Learning, to which the candidate responded with NLP. The interviewer then presented a scenario involving a client with text data of emails (customer feedback on their products). The discussion covered the complete Data Science lifecycle, proceeding step by step until the end. A few more questions on statistics and Machine Learning concepts were asked, with some delving into Deep Learning and CNNs. The candidate also asked questions about the work environment, clients, and different domains at ZS.

The candidate felt confident after the last round and later received a confirmation call and offer letter from HR.