Swiggy Machine Learning Engineer Interview Questions + Guide in 2025

Swiggy is a leading online food delivery platform in India, renowned for its innovative approach to logistics and customer service.

As a Machine Learning Engineer at Swiggy, you will be responsible for designing, implementing, and optimizing machine learning models that enhance various aspects of the business, including order prediction, customer engagement, and operational efficiency. Key responsibilities include developing algorithms to analyze large datasets, collaborating with cross-functional teams to translate business needs into machine learning solutions, and ensuring the scalability and reliability of deployed models. A successful candidate should possess strong programming skills in languages such as Python and R, a solid understanding of machine learning concepts, statistical analysis, and experience with big data technologies like Hadoop or Spark. Additionally, strong problem-solving abilities and effective communication skills are essential traits, as you will need to articulate complex technical concepts to non-technical stakeholders.

This guide aims to provide you with tailored insights and preparation strategies specific to Swiggy's expectations for the Machine Learning Engineer role, helping you stand out in your interview.

The interview process for a Machine Learning Engineer at Swiggy is structured to assess a variety of skills essential for the role, including technical expertise, problem-solving abilities, and cultural fit. The process typically consists of multiple rounds, each designed to evaluate different competencies.

1. Initial Screening

The first step in the interview process is an initial screening, which usually involves a review of your resume and a brief discussion with a recruiter. This conversation will focus on your background, relevant experiences, and motivation for applying to Swiggy. The recruiter will also assess your fit for the company culture and the specific role.

2. Technical Assessment

Following the initial screening, candidates are required to complete a technical assessment, often conducted through an online platform like HackerRank. This assessment typically includes SQL queries and may also feature a business case study. The questions range from basic to moderate difficulty, testing your understanding of data manipulation and analysis.

3. Technical Interviews

Candidates who pass the technical assessment will move on to one or more technical interviews. These interviews focus on machine learning concepts, algorithms, and coding skills. You may be asked to solve problems related to data structures, algorithms, and statistical methods. Expect questions that require you to explain your thought process and approach to problem-solving.

4. Applied Statistics and Business Case Studies

In this round, interviewers will delve into your knowledge of applied statistics and how it relates to business scenarios. You may be presented with case studies that require you to analyze data and propose solutions to real-world problems. This round assesses your ability to apply theoretical knowledge to practical situations, particularly in the context of Swiggy's operations.

5. Hiring Manager Round

The final round typically involves a discussion with the hiring manager or a senior leader within the team. This round focuses on your interpersonal skills, strengths, and weaknesses, as well as your alignment with Swiggy's goals and values. You may also be asked to discuss your previous projects and how they relate to the role you are applying for.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked in each round.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Swiggy typically consists of multiple rounds, including technical assessments, problem-solving scenarios, and discussions with hiring managers. Familiarize yourself with the structure of the interview, as it often includes a SQL test, a data science round focusing on machine learning concepts, and a business case study. Knowing what to expect will help you prepare effectively and manage your time during the interview.

Master SQL and Data Science Fundamentals

Given the emphasis on SQL in the interview process, ensure you have a strong grasp of SQL queries, including joins, subqueries, and window functions. Practice solving SQL problems on platforms like HackerRank to get comfortable with the types of questions you may encounter. Additionally, brush up on your knowledge of machine learning algorithms, statistics, and data analysis techniques, as these topics frequently arise in technical discussions.

Prepare for Business Case Studies

Swiggy values candidates who can think critically about business problems. Be prepared to tackle case studies that require you to analyze data and propose solutions. Familiarize yourself with Swiggy's business model and current challenges, as this will allow you to provide relevant insights during your case study discussions. Practice structuring your answers clearly and logically, as communication skills are crucial in these scenarios.

Showcase Problem-Solving Skills

During the interviews, you may be presented with guesstimate problems or hypothetical scenarios. Approach these questions methodically: clarify the problem, outline your thought process, and explain your reasoning step-by-step. Interviewers appreciate candidates who can articulate their problem-solving approach, even if they don't arrive at the "correct" answer.

Engage with Interviewers

Swiggy's interviewers often look for candidates who can engage in meaningful discussions. Don’t hesitate to ask clarifying questions or seek feedback during the interview. This demonstrates your interest in the role and your willingness to collaborate. Additionally, be prepared to discuss your past projects and experiences, as interviewers may want to understand how your background aligns with the role.

Be Ready for Technical Depth

Expect to dive deep into technical topics, especially in the data science and engineering rounds. Be prepared to explain complex concepts in simple terms, as you may need to communicate technical details to non-technical stakeholders. Practice explaining algorithms, statistical methods, and your previous projects in a way that is accessible to a broader audience.

Reflect on Your Fit with Company Culture

Swiggy values candidates who align with its culture of innovation and customer-centricity. Be prepared to discuss why you want to work at Swiggy and how your values align with the company's mission. Show enthusiasm for the role and the impact you hope to make within the organization.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity and reiterate your interest in the role. This not only demonstrates professionalism but also keeps you on the interviewers' radar as they make their decisions.

By following these tips and preparing thoroughly, you can enhance your chances of success in the interview process at Swiggy. Good luck!

Machine Learning Concepts

1. Explain the difference between supervised and unsupervised learning.

Understanding the fundamental concepts of machine learning is crucial. Be prepared to articulate the distinctions clearly and provide examples of each type.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in terms of labeled data and the types of problems they solve.

Example

“Supervised learning involves training a model on a labeled dataset, where the input-output pairs are known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering customers based on purchasing behavior.”

2. What is overfitting, and how can it be prevented?

This question tests your understanding of model performance and generalization.

How to Answer

Define overfitting and discuss techniques to mitigate it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. To prevent this, I use techniques like cross-validation to ensure the model generalizes well to unseen data, and I apply regularization methods to penalize overly complex models.”

3. Can you explain how a decision tree works?

This question assesses your knowledge of specific algorithms.

How to Answer

Describe the structure of a decision tree, how it splits data, and the criteria used for splitting.

Example

“A decision tree is a flowchart-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome. The tree splits the data based on feature values, using criteria like Gini impurity or information gain to determine the best splits.”

4. What is the purpose of feature engineering?

This question evaluates your understanding of data preprocessing.

How to Answer

Discuss the importance of feature engineering in improving model performance and the techniques you use.

Example

“Feature engineering is crucial as it transforms raw data into meaningful features that enhance model performance. Techniques I often use include normalization, encoding categorical variables, and creating interaction features to capture relationships between variables.”

5. Describe a machine learning project you have worked on.

This question allows you to showcase your practical experience.

How to Answer

Provide a brief overview of the project, your role, the challenges faced, and the outcomes.

Example

“I worked on a project to predict customer churn for an e-commerce platform. My role involved data preprocessing, feature selection, and model training using logistic regression. We achieved a 15% increase in retention rates by implementing targeted marketing strategies based on the model’s predictions.”

Statistics and Probability

1. What is a p-value, and what does it signify?

This question tests your understanding of hypothesis testing.

How to Answer

Define p-value and explain its role in determining statistical significance.

Example

“A p-value measures the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (typically < 0.05) indicates strong evidence against the null hypothesis, suggesting that the observed effect is statistically significant.”

2. Explain the Central Limit Theorem.

This question assesses your grasp of fundamental statistical concepts.

How to Answer

Discuss the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the original population distribution. This is crucial for making inferences about population parameters based on sample statistics.”

3. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and deletion.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I may use imputation techniques like mean or median substitution, or I might choose to delete rows or columns with excessive missing values to maintain data integrity.”

4. What is the difference between Type I and Type II errors?

This question tests your understanding of hypothesis testing errors.

How to Answer

Define both types of errors and their implications in decision-making.

Example

“A Type I error occurs when we reject a true null hypothesis, leading to a false positive, while a Type II error happens when we fail to reject a false null hypothesis, resulting in a false negative. Understanding these errors is vital for evaluating the reliability of statistical tests.”

5. Can you explain what a confidence interval is?

This question assesses your knowledge of statistical estimation.

How to Answer

Define confidence intervals and their significance in estimating population parameters.

Example

“A confidence interval is a range of values derived from a sample statistic that is likely to contain the true population parameter. For instance, a 95% confidence interval suggests that if we were to take many samples, approximately 95% of the intervals would contain the true mean.”

SQL and Data Manipulation

1. Write a SQL query to find the top 5 customers by total order value.

This question tests your SQL skills and ability to manipulate data.

How to Answer

Explain the logic behind your query and the SQL functions you would use.

Example

“I would use a query that aggregates the total order value for each customer and then orders the results in descending order to select the top 5. The query would look like: SELECT customer_id, SUM(order_value) AS total_value FROM orders GROUP BY customer_id ORDER BY total_value DESC LIMIT 5;”

2. How do you optimize a slow-running SQL query?

This question evaluates your problem-solving skills in database management.

How to Answer

Discuss various techniques for query optimization, such as indexing and query restructuring.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Techniques I might employ include adding appropriate indexes, rewriting the query for efficiency, and ensuring that I’m only selecting the necessary columns to reduce data load.”

3. Explain the difference between INNER JOIN and LEFT JOIN.

This question tests your understanding of SQL joins.

How to Answer

Define both types of joins and provide examples of when to use each.

Example

“An INNER JOIN returns only the rows with matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in NULLs for non-matching rows. I would use INNER JOIN when I need only the intersecting data, and LEFT JOIN when I want to retain all records from the left table regardless of matches.”

4. What are window functions in SQL, and how are they used?

This question assesses your advanced SQL knowledge.

How to Answer

Define window functions and explain their applications in data analysis.

Example

“Window functions perform calculations across a set of table rows related to the current row. They are useful for tasks like running totals or moving averages. For example, using SUM() OVER (PARTITION BY category ORDER BY date) allows me to calculate cumulative sales by category over time.”

5. How would you handle duplicate records in a dataset?

This question evaluates your data cleaning skills.

How to Answer

Discuss methods for identifying and removing duplicates in SQL.

Example

“I would identify duplicates using a query that groups records by key fields and counts occurrences. Then, I would use a DELETE statement with a CTE or a subquery to remove duplicates while retaining one instance of each record.”