Company: Zomato
Designation: Data Scientist
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: Competitive, based on experience

Zomato, a leading restaurant aggregator and food-delivery platform, connects millions of users with restaurants worldwide. Known for its robust data infrastructure and user-centric approach, Zomato relies on data scientists to optimize recommendations, delivery logistics, and customer experiences. If you’re preparing for a Data Science role at Zomato, here’s a detailed breakdown of their interview process and the questions you can expect.

If you are preparing for a Data Scientist Job at Zomato, make sure to go through the following questions.

Interview Process

The Zomato Data Science interview process typically consists of 5 rounds, each designed to assess technical expertise and problem-solving skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: Discuss past projects and solve introductory coding/SQL problems.

Round 2 – Face-to-Face Technical Round

Focus: Advanced SQL, Statistics, and foundational Machine Learning.
Format: Solve problems on a whiteboard or shared document (e.g., query optimization, hypothesis testing).

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: Explain your approach, tools, challenges, and impact of your work.

Round 4 – Case Studies

Focus: Real-world business problems (e.g., delivery time optimization, user engagement).
Format: Propose data-driven solutions and defend your strategy.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and alignment with Zomato’s goals.
Format: Behavioral questions and discussions about long-term career aspirations.

Difficulty of Questions

SQL – 7/10

1) How can you find employees who have a higher salary than their manager?

SELECT e1.name AS employee, e1.salary, e2.name AS manager, e2.salary AS manager_salary
FROM employees e1
JOIN employees e2 ON e1.manager_id = e2.employee_id
WHERE e1.salary > e2.salary;

2) How can you find customers who have purchased all available products at least once?

SELECT customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(DISTINCT product_id) = (SELECT COUNT(*) FROM products);

3) How do you find the product that has been ordered the most?

SELECT product_id, SUM(quantity) AS total_quantity
FROM order_details
GROUP BY product_id
ORDER BY total_quantity DESC
LIMIT 1;

4) How can you identify customers who have placed orders in consecutive months?

SELECT customer_id, order_id, order_date,
LAG(MONTH(order_date)) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_month
FROM orders
HAVING prev_month IS NOT NULL AND MONTH(order_date) = prev_month + 1;

5) How do you find the product with the lowest number of sales?

SELECT product_id, SUM(quantity) AS total_sold
FROM order_details
GROUP BY product_id
ORDER BY total_sold ASC
LIMIT 1;

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R/Python – 8/10

1) Write a Python function to compute the moving average of a given list of numbers with a specified window size.

2) Write a Python function to find the N largest values in a specific Pandas DataFrame column.

3) Write a Python function to count the number of unique values in a given Pandas DataFrame column.

4) Write a Python function to perform K-Means clustering on a given dataset using scikit-learn.

5) Write a Python function to compute the TF-IDF scores for words in a given text document using scikit-learn.

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Statistics/ML

1) Is it better to have too many false negatives or too many false positives?

The answer depends on the use case.

• False Negatives (FN): These occur when the model fails to identify a positive case. In medical diagnoses (e.g., cancer detection), false negatives are worse because missing a disease can have life-threatening consequences.
• False Positives (FP): These occur when a model incorrectly identifies a negative case as positive. In spam detection, false positives are more acceptable because marking a genuine email as spam is less harmful than missing an important one.

2) Steps to Validate a Predictive Model using Multiple Regression

To ensure a reliable and accurate model, follow these steps:

1. Split the Data – Divide the dataset into training (80%) and testing (20%) sets.
2. Check Assumptions – Ensure linearity, no multicollinearity, homoscedasticity, and normality of residuals.
3. Cross-Validation – Use k-fold cross-validation to assess model generalizability.
4. Evaluate Performance – Measure R², Adjusted R², RMSE, and MAE to check accuracy.
5. Residual Analysis – Ensure residuals are randomly distributed with a mean of zero.
6. Check for Overfitting – If training accuracy is much higher than test accuracy, consider regularization (L1/L2).

3) Should You Spend 5 Days for 90% Accuracy or 10 Days for 100% Accuracy?

It depends on the business impact:

• If 90% accuracy is good enough to make a decision, spending extra time isn’t necessary.
• If 100% accuracy significantly reduces risk or cost, investing more time is justified.

4) Important Data for Business & How to Collect It

Data importance depends on the industry:

• E-commerce: User behavior, purchase history, abandoned carts
• Finance: Credit scores, transaction history, fraud indicators
• Healthcare: Patient records, diagnostic reports, treatment responses

🔹 How to Collect Data?

1. APIs & Web Scraping – Extract live data (e.g., stock prices, social media trends).
2. Surveys & Feedback Forms – Collect direct customer insights.
3. Databases & CRM Systems – Store transaction details and customer interactions.
4. IoT Sensors & Logs – Gather real-time operational data.

5) How to Measure If an Algorithm Change is an Improvement?

 Key Steps:

1. Compare Metrics – Use accuracy, precision, recall, or RMSE.
2. A/B Testing – Compare old vs. new models on live data.
3. Cross-Validation – Ensure generalizability across different data samples.
4. Business Impact – Does it improve speed, cost, or user experience?

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Case Study

Problem Statement:

Zomato wants to predict restaurant demand in different locations to optimize delivery efficiency and reduce customer wait times. Your task as a Data Scientist is to analyze past order data, identify demand trends, and provide insights to improve food delivery operations.

Dataset Overview:

You have access to a dataset containing historical food delivery orders and restaurant performance metrics. The dataset includes:

• Order_ID – Unique identifier for each order
• Order_DateTime – Timestamp of the order
• Restaurant_ID – Unique identifier for each restaurant
• Restaurant_Location – City or locality of the restaurant
• Cuisine_Type – Type of cuisine (Indian, Chinese, Italian, etc.)
• Order_Amount – Total amount spent on the order
• Delivery_Time (mins) – Time taken for the order to be delivered
• Customer_Rating – Rating given by the customer (1-5)
• Delivery_Partner_ID – Unique identifier for the assigned delivery partner
• Weather_Condition – Weather status at the time of order (Clear, Rainy, Cloudy, etc.)
• Order_Cancellation_Status – 1 if the order was canceled, 0 if not

Key Questions to Answer:

1. What factors influence restaurant demand and order volume?

• Do peak meal times (lunch/dinner) see higher orders?
• Are orders affected by weather conditions?
• Do certain cuisine types have higher demand in specific locations?

2. How can Zomato optimize food delivery operations?

• Can Zomato predict high-demand areas and allocate more delivery partners?
• How can Zomato reduce delivery time and improve customer satisfaction?
• Does the delivery partner’s efficiency impact customer ratings?

3. How can Zomato reduce order cancellations and improve retention?

• Are long delivery times causing order cancellations?
• Do low-rated restaurants have a higher churn rate?
• What strategies can improve repeat customer engagement?

Key Insights & Business Recommendations

1. Understanding Restaurant Demand Trends

• Peak Hours Drive High Demand: Order volumes are highest during lunch (12–2 PM) and dinner (7–10 PM), requiring better restaurant preparation and delivery scheduling.
• Weather Impacts Order Volume: On rainy days, orders increase, but delivery times also rise, leading to higher cancellations and lower ratings.
• Cuisine-Specific Demand Patterns: Certain cuisines, like Chinese and Pizza, have higher night-time demand, while Indian meals dominate lunch orders.

2. Optimizing Food Delivery Operations

• AI-Based Demand Prediction: Zomato should use real-time order prediction models to anticipate high-demand areas and allocate delivery partners accordingly.
• Dynamic Delivery Partner Allocation: Assigning delivery partners based on traffic conditions and real-time demand can reduce delivery time and increase efficiency.
• Optimizing Delivery Routes with AI: Using route optimization algorithms, Zomato can reduce delivery delays caused by traffic congestion.

3. Reducing Order Cancellations & Improving Retention

• Reducing Delivery Delays: By ensuring restaurants prepare food faster and delivery partners take optimized routes, Zomato can reduce late deliveries and cancellations.
• Boosting Customer Retention with Personalized Offers: Offering discounts to repeat customers and using personalized recommendations can improve customer retention.
• Improving Restaurant Ratings with Customer Feedback Analysis: Identifying low-rated restaurants and providing them with operational insights can enhance food quality and service.

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