Swiggy Senior Data Scientist Interview Questions 2026
Crack the Swiggy Senior Data Scientist interview with these top 10 questions, expert tips, and a 2-week preparation plan. Average salary: ₹10-20 LPA.
About Swiggy's Analytics Team
Swiggy uses data analytics extensively for delivery optimization, restaurant recommendations, and customer retention. Growing data team.
Skills Required for Swiggy Senior Data Scientist Interview
Swiggy evaluates candidates on these technical and analytical skills. Ensure you are comfortable with at least 70% of this list before applying.
Top 10 Swiggy Senior Data Scientist Interview Questions
Tell me about yourself and why you want to be a Senior Data Scientist at Swiggy.
Hint: Focus on your data experience, relevant projects, and genuine interest in the company's data challenges.
Write a SQL query to find the top 5 customers by revenue in the last 30 days.
Hint: Use SELECT, JOIN, WHERE with date filter, GROUP BY, ORDER BY DESC, and LIMIT 5.
How would you clean a dataset with 20% missing values in a key column?
Hint: Discuss imputation strategies (mean/median/mode), domain-specific defaults, or dropping rows based on business impact.
Describe a complex data analysis project you completed end-to-end.
Hint: Use the STAR method: Situation, Task, Action, Result. Quantify the business impact.
Explain the difference between a LEFT JOIN and INNER JOIN with an example.
Hint: INNER JOIN returns only matching rows from both tables. LEFT JOIN returns all rows from the left table and matched rows from the right.
Write a Python function using Pandas to calculate a 7-day moving average.
Hint: Use df[column].rolling(7).mean() in Pandas. Handle edge cases at the start of the series.
Swiggy has seen a 15% drop in a key metric this week. How do you diagnose the issue?
Hint: Structured approach: Check data quality first, then segment (device, region, channel), compare time periods, correlate with product releases or external events.
Explain the concept of data normalization and when it matters.
Hint: Normalization scales features to a common range, important for distance-based algorithms like KNN or clustering.
How would you build a BI dashboard to track Swiggy's key business metrics?
Hint: Identify KPIs with stakeholders, design the data model, build the transformation layer, create the visualization, and schedule refresh.
Where do you see data analytics heading in the next 3–5 years and how are you preparing?
Hint: Mention AI-augmented analytics, real-time analytics, data mesh, and the growing importance of data literacy across organizations.
How to Prepare for Swiggy Senior Data Scientist Interview in 2 Weeks
Week 1: Technical Foundation
- ✓Practice 50+ SQL queries covering JOINs, CTEs, window functions, and aggregations
- ✓Review Python and Deep Learning fundamentals and practice problems
- ✓Study Swiggy's business model and recent analytics case studies
Week 2: Mock Practice & Company Research
- ✓Complete 3+ mock case study interviews with business metric analysis
- ✓Practice communicating your portfolio projects using the STAR format
- ✓Review Swiggy's recent products, features, and data announcements
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