Swiggy data science candidates report SQL window functions, Python and pandas, ML fundamentals, resume deep-dives, and product problems such as delivery ETA prediction. Reports include full-time and intern interviews.
$32K
Avg. Base Comp
$42K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Swiggy data scientist candidates describe a mix of coding, machine learning theory and business problem-solving, usually anchored in their own projects. Five reports cover both full-time and internship interviews, and four of them ended without an offer.
SQL window functions and aggregations come up repeatedly. One full-time candidate was asked about rank functions, HAVING and GROUP BY. Another had to write a CTE with window functions to find the two highest-paid employees in each department. An intern reported an nth-highest-salary query, and another intern mentioned joins, aggregations and top-performer style questions on delivery data.
Coding is described as light. Candidates mention easy LeetCode-style tasks such as sorting, finding the second largest number or merging sorted arrays. Python questions also covered pandas functions like apply, get_dummies and concat, and one intern wrote NumPy code for assigning points to cluster centroids.
On machine learning, candidates report L1 versus L2 regularization, classifier trade-offs, cross-entropy loss and K-means. They also report linear regression from first principles and Bayes theorem. Resume walkthroughs can be deep. One intern was asked why log transforms were used, why a decision tree beat a random forest, and to derive the 37% OOB figure.
One full-time candidate's final round was problem-solving: features for ETA prediction, improving Blinkit search, and allocating full-time and part-time delivery partners. Another intern also reported applying ML to delivery time, fraud, demand forecasting and recommendations. Prepare to explain your reasoning aloud, since candidates describe collaborative interviewers.
Synthesized from 5 candidate reports by our editorial team.
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Write a function can_shift to return whether or not A can be shifted some number of places to get B
| Question | |
|---|---|
| Size of Joins | |
| Z and t-Tests | |
| Forecasting New Year Revenue | |
| Increased Cancellations | |
| Choosing k | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Longest Streak Users | |
| Experiment Validity | |
| Upsell Transactions | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Subscription Overlap | |
| First to Six | |
| Button AB Test | |
| Random SQL Sample | |
| Paired Products | |
| Month Over Month | |
| Merge Sorted Lists | |
| Fair Coin | |
| Manager Team Sizes | |
| Download Facts | |
| Top 3 Users | |
| SELECTive Wine Connoisseur | |
| Decreasing Comments |
Synthesized from candidate reports. Individual experiences may vary.
Two full-time candidates report a first round of easy coding plus SQL. Examples include sorting, second-largest number and merging sorted arrays. SQL covered rank functions, HAVING, GROUP BY, CTEs and window functions. One candidate also reports pandas questions. Expect this to be a practical screen rather than hard algorithm work.
Candidates report ML conversations on L1 versus L2 regularization, classifiers, overfitting and cross-entropy loss. One round began with about 15 minutes on a resume project. Interns report resume-driven probing of preprocessing, metrics, K-means, linear regression and Bayes theorem, so you should be ready to justify each modeling choice.
Intern reports describe applied Python, NumPy, pandas and SQL tasks. These include centroid assignment for a clustering setup, nth-highest salary queries, data cleaning, grouping and aggregation. One intern said the DSA portion focused on logical, efficient solutions rather than very hard competitive-programming problems.
One on-campus candidate describes a third problem-solving and managerial round on ETA features, Blinkit search improvements and delivery-partner allocation. Another candidate describes the third round as HR and behavioral. One intern also reported product-oriented ML questions, so prepare for both business reasoning and teamwork discussion.
