Chewy, a leading online retailer for pet products, is known for its dedication to customer service and innovative approach to solving business challenges. The company has quickly become a go-to destination for pet parents looking for quality products and an exceptional shopping experience.

As a Data Scientist at Chewy, you will immerse yourself in a dynamic role that involves building advanced machine learning models, designing data-driven solutions, and engineering pipelines that drive the company's core objectives. You'll work on varied tasks, from customer segmentation and fraud detection to supply chain optimization and demand forecasting.

In this guide, we'll navigate the interview process, common questions, and valuable tips to help you succeed. Let's get started with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Chewy as a Data Scientist. Whether you were contacted by a Chewy recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Chewy Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process. It's important to remain professional and flexible, as some experiences indicate that scheduling can sometimes be inconsistent.

In some cases, the hiring manager may also be present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

Coding Assessment and Technical Interviews

The next step typically involves a combination of coding assessments and technical interviews. Initially, you might be given a coding assessment focusing on Python and SQL to evaluate your data manipulation and analysis skills. Questions may revolve around linear regression models, ETL pipelines, and general Python/SQL queries.

Following the coding assessment, you will likely face a few technical interview rounds. These interviews will delve deeper into your understanding of machine learning algorithms, data analysis, statistical modeling, and system design. Be prepared to discuss case studies and be evaluated on your problem-solving skills. Presentation of previous projects and understanding of machine learning concepts like bias-variance tradeoff and predictive modeling might also be part of this stage.

Final Onsite Interview Rounds

If you successfully navigate the technical rounds, you'll be invited to attend the final onsite interview (which might be conducted virtually as well). This will typically include multiple interview rounds, involving presentations, behavioral questions, and in-depth technical discussions to gauge your fit for the team and the company.

Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates. Ensure to prepare for this stage thoroughly as it encompasses diverse assessment aspects ranging from technical skills to cultural fit.

Quick Tips For Chewy Data Scientist Interviews

• Keep Communication Clear and Documented: Given the multiple accounts of communication mishaps, ensure all appointments and interview schedules are well-documented and confirmed via email.
• Brush Up on Machine Learning and Python: Be proficient in machine learning techniques and Python programming, as these are core to the technical assessments.
• Be Prepared for Behavioral Questions: Apart from technical skills, behavioral questions are significant in the interview process. Be ready to discuss your previous project experiences and how you handle both successes and failures.

Sign up on Interview Query

Typically, interviews at Chewy vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Create a function to parse the most frequent words in poems. Create a function to parse the most frequent words in poems. Return a dictionary where keys are the frequency of words and values are lists of words with that frequency. Process all words as lowercase and ignore punctuation.

• Create a function to find words not common in two sentences. Create a function that returns a list of words not common in two sentences. Treat words case-insensitively and assume no punctuation or extra spaces.

• What metrics would you use to determine the value of each marketing channel? Given all the different marketing channels and their respective costs at Mode, a B2B analytics dashboard company, what metrics would you use to evaluate the value of each marketing channel?

• How would you measure the success of the banner ad strategy? An online media company wants to experiment with adding web banners in the middle of its reading content to monetize web traffic. How would you measure the success of this banner ad strategy?

• What do you recommend for a $1 million direct mail program investment? Your team wants to invest $1 million in a direct mail program for the first time. What do you recommend for the short and long term, and how will you measure the direct impact of this investment?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees to form a forest. Additionally, discuss the advantages of using random forest over logistic regression in certain scenarios.

• Which model performs better for predicting Airbnb booking prices: linear regression or random forest regression? Consider building a model to predict booking prices on Airbnb. Compare the performance of linear regression and random forest regression, and explain which model would likely perform better and why.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• What is the difference between covariance and correlation? Provide an example. Describe the difference between covariance and correlation, and provide an example to illustrate the distinction.

• What are time series models? Why do we need them when we have less complicated regression models? Explain what time series models are and why they are necessary despite the availability of simpler regression models.

• How would you determine if the difference between this month and the previous month in a time series dataset is significant? Given a time series dataset grouped monthly for the past five years, describe how you would assess whether the difference between this month and the previous month is significant.

• How would you address a manager's complaint about a packet filling machine not functioning correctly? A manager reports that a machine, which is supposed to weigh and pack 25 packets into a box, is malfunctioning. Customers are complaining about incorrect packet counts. How would you investigate and resolve this issue?

What is the average salary for a Data Scientist at Chewy?

Q: What is the interview process at Chewy like for a Data Scientist position? The interview process at Chewy typically involves several stages, including an initial phone screen with HR, followed by technical interviews that may cover Python, SQL, and machine learning concepts. Candidates may also face a virtual onsite interview, which includes multiple rounds of interviews focusing on technical skills, behavioral questions, and detailed discussions about previous projects.

Q: What kind of questions can I expect during the Chewy Data Scientist interview? Expect questions that cover a broad range of topics including SQL and Python coding, machine learning case studies, and statistical analysis. You might also be asked behavioral questions like "Tell me about a time when you saw failure and how did you handle it?" and technical questions such as building linear regression models under changing requirements.

Q: What qualifications are required for a Data Scientist at Chewy? Candidates typically need a Bachelor's degree in a relevant field such as Computer Science, Engineering, or Mathematics, along with 3 years of experience. A Master's degree with 1 year of experience is also acceptable. Key skills include Python programming, machine learning techniques, SQL, and experience with data pipeline best practices.

Q: What is the work culture like at Chewy? Chewy promotes a culture that values creativity, teamwork, and individual growth. Employees are encouraged to think big, thrive on delivering results, and become their best selves. The company also emphasizes inclusivity and values the diverse perspectives of its team members.

Q: How can I best prepare for an interview at Chewy? To prepare for your interview at Chewy, review common interview questions and hone your technical skills, particularly in Python, SQL, and machine learning. Practice with platforms like Interview Query for realistic interview scenarios, and ensure you're equipped to discuss your past projects and experiences in detail.

Conclusion

The experience of interviewing for a Data Scientist position at Chewy reveals a mixed bag of narratives. While some candidates found the process disorganized and frustrating, others appreciated its structure and content. The role itself is rich with opportunities, demanding a wide array of skills from Python and SQL programming to advanced machine learning and data analysis techniques. Chewy is dedicated to creating an inclusive and supportive work environment, with competitive salaries and comprehensive benefits.

Given these diverse insights, if you're preparing for an interview at Chewy, it's crucial to arm yourself with detailed knowledge and familiarity with various interview stages and potential questions. For invaluable resources and insights, check out our main Chewy Interview Guide. We cover a plethora of interview questions and also offer guides for other roles. At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to tackle every challenge.

You can explore all our company interview guides for better preparation. If you have any questions, don’t hesitate to reach out.

Good luck with your interview!