Swiggy Data Scientist interview typically runs 2 rounds: resume deep-dive and ML coding/SQL. It usually takes about 2 rounds and is highly resume-driven and technical.
$2300K
Avg. Base Comp
$3520K
Avg. Total Comp
2
Typical Rounds
1-2 weeks
Process Length
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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Swiggy process.
My interview process at Swiggy for the Data Scientist role had three rounds spread over a couple of weeks. The first round focused on DSA and SQL. I got two easy-level LeetCode questions — one was sorting an array, and the other asked me to find the second largest number. Then there were three SQL problems testing knowledge of rank functions, having clauses, and group by operations. The interviewer was supportive and didn't rush me through the problems, which helped me think through the logic clearly. The second round shifted entirely to machine learning and data science concepts. They asked about regularization techniques, specifically the differences between L1 and L2, and dived into classical ML classifiers and how to think about model selection. This round felt more conversational — less about coding, more about understanding my reasoning. The third round was with HR and covered real-time behavioral questions about how I'd handle challenges and work within a team. What stood out most was how collaborative the interviewers were. Even when I got stuck, they guided me toward the solution without just handing it to me, which made the whole experience feel less adversarial and more like a genuine technical discussion. By the end, I felt I'd shown my capabilities, though I wasn't selected. In retrospect, I wish I'd spent more time drilling SQL window functions beforehand — those rank and aggregate questions came up fast, and I could have been sharper on the syntax.
Prep tip from this candidate
Focus heavily on SQL window functions like rank, dense_rank, and group by aggregations before the first round — those appeared consistently. For the ML round, be ready to explain L1 vs L2 regularization clearly and discuss trade-offs between different classifiers, not just define them.
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Topics based on recent interview experiences.
Featured question at Swiggy
Write a function can_shift to return whether or not A can be shifted some number of places to get B
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| Size of Joins | |
| Z and t-Tests | |
| Forecasting New Year Revenue | |
| Increased Cancellations | |
| Choosing k | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Top Three Salaries | |
| Upsell Transactions | |
| Button AB Test | |
| First to Six | |
| Merge Sorted Lists | |
| Compute Deviation | |
| Month Over Month | |
| Download Facts | |
| SELECTive Wine Connoisseur | |
| Average Quantity | |
| 500 Cards | |
| Network Experiment Design | |
| Random SQL Sample | |
| Longest Streak Users | |
| Manager Team Sizes |
Synthesized from candidate reports. Individual experiences may vary.
The first round is a very detailed walkthrough of your resume and past projects. Expect the interviewer to pick one project and drill into the problem statement, data source, preprocessing choices, feature engineering, model selection, and evaluation metrics, along with theory questions on topics like correlation, class imbalance, decision trees vs. random forests, OOB score, TF-IDF, bag of words, and logistic regression.
The second round focuses on practical implementation in Python, NumPy, Pandas, and SQL, with no DSA. Candidates may be asked to solve a clustering-style coding problem, such as generating synthetic data and assigning points to the nearest centroid, followed by a SQL query like finding the employee with the 5th highest salary.
Close preparation with examples that show ownership, communication, and how you work with cross-functional partners or technical peers. The available candidate evidence is sparse, so this stage is framed as a practical preparation bucket rather than a claim that every candidate saw a separate formal round. Where the source evidence blended final steps together, this stage captures the final evaluation themes without adding unsupported company-specific claims.
