Swiggy Data Scientist Interview Questions and Process 2026

Cracking the Swiggy Data Scientist Interview

Data Science is the brain behind Swiggy's operations. Predicting delivery times (ETAs), recommending restaurants, and assigning delivery partners efficiently all rely on complex Machine Learning models. The interview process tests your statistical foundation, your coding skills, and your ability to apply ML to real-world logistics problems.

Phase 1: Statistics and Coding Fundamentals

Before you build models, you must prove your mathematical and coding foundations.

• Probability & Statistics: Expect questions on probability distributions, A/B testing, p-values, and hypothesis testing. "Explain the Central Limit Theorem and why it is useful."
• Python/SQL Coding: You must be fluent in SQL and Python (Pandas/NumPy). You will be given a messy dataset and asked to write a script to clean it, handle missing values, and extract specific features.

Phase 2: Machine Learning Theory and Algorithms

This round evaluates your understanding of how models actually work under the hood.

• Model Selection: "When would you choose a Random Forest over a Gradient Boosting Machine (GBM)?"
• Under the Hood: You may be asked to explain the math behind an algorithm. "Explain the cost function in Logistic Regression." "How does a Decision Tree decide where to split a node?"
• Evaluation Metrics: "Your model has a 99% accuracy rate but is performing terribly in production. Why?" You must understand Precision, Recall, F1-Score, and ROC-AUC, especially for imbalanced datasets (e.g., detecting fraudulent transactions).

Phase 3: Applied Data Science and System Design (The Decider)

This round tests how you translate a business problem into a scalable Machine Learning solution.

• The Scenario: "Design the ML architecture to predict the Estimated Time of Arrival (ETA) for a food delivery."
• The Breakdown: The interviewer expects you to discuss:
  • Feature Engineering: What data will you use? (Traffic data, weather, restaurant preparation time, driver speed).
  • Model Choice: Why might a Deep Learning model (like an LSTM) or an XGBoost model work best here?
  • Production Deployment: How do you serve this model in real-time to millions of users? (Handling latency, model drift, and retuning).

Top 3 Technical Questions to Prepare

1. Explain how you would design a personalized restaurant recommendation engine for a new user with no order history (The Cold Start problem).
2. How do you design an A/B test to determine if a new surge pricing algorithm increases overall revenue without losing customers?
3. Explain the concept of L1 and L2 regularization. When would you use one over the other to prevent overfitting?