Swiggy Data Scientist Interview Guide

We’ve seen Swiggy lean hard into whether candidates can explain their own work under pressure, not just name the right tools. In the experience shared here, the interviewer kept pulling on the thread of one project until the candidate had to justify the data source, preprocessing, log transforms, and even basic correlation intuition. That pattern tells us Swiggy is looking for people who can defend modeling decisions with clarity, especially when the choice isn’t the textbook one. The same theme showed up again in the NLP discussion, where the candidate had to walk through why TF-IDF fit the problem, why random forest beat logistic regression in that case, and how the evaluation metric changed depending on the business setting.

A second signal is that Swiggy seems to care about mechanics, not memorization. The candidate wasn’t just asked what OOB score means; they were pushed to derive why it lands around 37%. That kind of follow-up is a recurring marker of this process: if you mention a concept, expect to unpack the math or the tradeoff behind it. We also notice a practical streak in the coding round — clustering setup, NumPy/Pandas, and a simple but time-sensitive SQL query — which suggests the bar is less about exotic algorithms and more about whether you can translate ML thinking into working code and clean reasoning. Candidates who do best here are the ones whose resumes can survive a detailed audit.