Chewy Research Scientist Interview Questions + Guide in 2025

Chewy is a rapidly growing company dedicated to providing outstanding services and products for pet owners, fostering a culture that values customer experience and innovation.

As a Research Scientist at Chewy, you will play a crucial role in the Supply Chain Research and Planning Team, leveraging your background in supply chain analytics and data science to develop and implement sophisticated models and algorithms. Your key responsibilities will include investigating large datasets to derive insights and recommendations, collaborating with various functional areas such as Demand Planning and Transportation, and spearheading process improvement initiatives to enhance supply chain metrics. A strong analytical mindset, proficiency in Python and SQL, and familiarity with optimization tools like Gurobi or CPLEX are essential to thrive in this role. Additionally, the ability to communicate complex technical concepts to non-technical stakeholders is vital, as is the capacity to manage multiple projects efficiently.

This guide will help you prepare for your interview by highlighting the skills and experiences that Chewy values, enabling you to present yourself as a strong candidate who aligns with the company’s mission and operational needs.

The interview process for a Research Scientist at Chewy is designed to assess both technical and behavioral competencies, ensuring candidates are well-suited for the dynamic environment of the company. The process typically unfolds in several structured stages:

1. Initial Phone Screen

The first step involves a phone interview with a recruiter, lasting about 30 minutes. This conversation serves to gauge your general background, interest in the role, and alignment with Chewy's values. Expect standard questions about your experience, motivation for applying, and your understanding of the company and its mission. This is also an opportunity for you to ask about the company culture and the specifics of the role.

2. Technical Assessment

Following the initial screen, candidates usually undergo a technical assessment, which may be conducted via a coding platform or through a live coding session. This round focuses on your proficiency in programming languages such as Python and SQL, as well as your ability to solve problems related to machine learning, optimization, and data manipulation. You may be asked to tackle algorithmic challenges or case studies relevant to supply chain issues.

3. Behavioral Interview

After successfully completing the technical assessment, candidates typically participate in a behavioral interview. This round is designed to evaluate your soft skills, teamwork, and cultural fit within Chewy. Expect questions that explore your past experiences, how you handle challenges, and your approach to collaboration and communication with stakeholders.

4. Final Panel Interview

The final stage usually consists of a panel interview, which can last several hours and includes multiple interviewers from different departments. This round often combines technical questions, case studies, and discussions about your previous projects. You may be asked to present your work or explain your thought process in solving complex supply chain problems. The panel will assess not only your technical expertise but also your ability to communicate effectively with non-technical stakeholders.

5. Follow-Up and Offer

After the final interview, candidates can expect a follow-up from the recruiter regarding the outcome. While some candidates report a quick turnaround, others have experienced delays in communication. If selected, you will receive an offer detailing the role, compensation, and benefits.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may arise during the process. Here are some examples of the topics you might encounter.

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

Emphasize Your Passion for Pets and Customer-Centric Solutions

Chewy's culture is deeply rooted in a love for pets and a commitment to customer satisfaction. During your interview, make sure to express your enthusiasm for the pet industry and how your work can contribute to enhancing customer experiences. Share personal anecdotes or experiences that highlight your passion for pets and how it aligns with Chewy's mission. This will resonate well with the interviewers and demonstrate that you are a good cultural fit.

Prepare for a Mix of Technical and Behavioral Questions

Expect a blend of technical, coding, and behavioral questions during the interview process. Be ready to discuss your experience with machine learning, optimization, and data analysis, as well as your proficiency in Python and SQL. Additionally, prepare to answer behavioral questions that assess your problem-solving skills and ability to work collaboratively. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples from your past experiences.

Showcase Your Analytical and Problem-Solving Skills

Given the role's focus on supply chain optimization, be prepared to discuss specific projects where you developed and implemented models or algorithms to solve complex problems. Highlight your experience with optimization solvers like Gurobi or CPLEX, and be ready to explain your thought process in tackling supply chain challenges. Interviewers will appreciate candidates who can translate complex data into actionable insights, so practice articulating your analytical approach.

Understand Chewy's Supply Chain Challenges

Familiarize yourself with the current trends and challenges in supply chain management, particularly in the e-commerce and pet industry. Research Chewy's supply chain strategies and be prepared to discuss how your skills and experiences can help address these challenges. This knowledge will demonstrate your proactive approach and genuine interest in contributing to Chewy's success.

Be Ready for a Structured Interview Process

The interview process at Chewy can be extensive, often involving multiple rounds with different stakeholders. Stay organized and be prepared for a potentially lengthy process. If you encounter any scheduling issues or delays, remain patient and professional. Follow up politely if you haven't heard back after a reasonable time, as communication can sometimes be a challenge.

Communicate Clearly and Confidently

Throughout the interview, focus on clear and confident communication. Given the technical nature of the role, it's essential to convey complex ideas in a way that non-technical stakeholders can understand. Practice explaining your projects and technical concepts in simple terms, as this will be a valuable skill in your role at Chewy.

Prepare for Cultural Fit Questions

Chewy values a collaborative and inclusive work environment. Be prepared to discuss how you work in teams, handle feedback, and contribute to a positive workplace culture. Reflect on your past experiences and think about how you can demonstrate your alignment with Chewy's values during the interview.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Research Scientist role at Chewy. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Chewy. Candidates should focus on demonstrating their technical expertise in machine learning, optimization, and data analysis, as well as their ability to communicate complex concepts clearly. Additionally, showcasing a passion for the company's mission and values can set candidates apart.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Provide clear definitions of both supervised and unsupervised learning, along with examples of each. Highlight the types of problems each approach is best suited for.

Example

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

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Discuss a specific project, the methodologies used, and the challenges encountered. Emphasize how you overcame these challenges.

Example

“I worked on a project to predict customer churn using logistic regression. One challenge was dealing with imbalanced data. I addressed this by implementing SMOTE to generate synthetic samples of the minority class, which improved our model's accuracy significantly.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a fraud detection model, I focus on recall to ensure we catch as many fraudulent cases as possible, even if it means having some false positives.”

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

Understanding overfitting is essential for developing robust models.

How to Answer

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

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization. I prevent it by using techniques like cross-validation to ensure the model performs well on unseen data and applying L1 or L2 regularization to penalize overly complex models.”

Optimization

1. Can you explain what linear programming is and provide an example of its application?

This question assesses your knowledge of optimization techniques.

How to Answer

Define linear programming and describe a real-world application, such as supply chain optimization.

Example

“Linear programming is a mathematical method for determining a way to achieve the best outcome in a given mathematical model. For example, in supply chain management, it can be used to minimize costs while meeting demand and capacity constraints by optimizing the allocation of resources.”

2. What optimization solvers are you familiar with, and how have you used them?

This question gauges your practical experience with optimization tools.

How to Answer

List the solvers you have used and describe specific projects where they were applied.

Example

“I have experience with Gurobi and CPLEX. In a recent project, I used Gurobi to optimize our inventory levels across multiple warehouses, which resulted in a 15% reduction in holding costs while maintaining service levels.”

3. How would you approach a complex supply chain optimization problem?

This question tests your problem-solving and analytical skills.

How to Answer

Outline a structured approach, including data collection, model formulation, and solution analysis.

Example

“I would start by gathering relevant data on demand, supply, and constraints. Next, I would formulate the problem as a mathematical model, using linear programming or mixed-integer programming. After solving the model, I would analyze the results to ensure they align with business objectives and make adjustments as necessary.”

Statistics & Data Analysis

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

This question assesses your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation or removal.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If it's minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or, if appropriate, removing those records entirely to avoid bias.”

2. Explain the concept of A/B testing and its importance.

This question evaluates your understanding of experimental design.

How to Answer

Define A/B testing and discuss its significance in decision-making.

Example

“A/B testing is a method of comparing two versions of a webpage or product to determine which one performs better. It’s crucial for data-driven decision-making, as it allows us to test hypotheses and make informed changes based on user behavior.”

3. What statistical methods do you use to analyze data?

This question gauges your statistical knowledge and analytical skills.

How to Answer

Mention various statistical methods and when to use them, such as regression analysis, hypothesis testing, and ANOVA.

Example

“I frequently use regression analysis to understand relationships between variables, hypothesis testing to validate assumptions, and ANOVA for comparing means across multiple groups. Each method provides valuable insights depending on the research question at hand.”