Wayfair Data Scientist Interview Guide

The resume review process is usually the first step in the interview process for a data scientist role at Wayfair. During this step, Wayfair's recruiters and hiring managers will review the candidate's resume to assess their qualifications, skills, and experience. They may also look for keywords related to the data science field, such as machine learning, data analysis, and statistical modelling, to determine if the candidate has relevant experience for the role.

The recruiters and hiring managers will also look for relevant academic degrees, such as a Master's or PhD in a quantitative field, and any relevant certifications or courses related to data science. They typically also look for evidence of strong technical skills, such as programming languages and data visualization tools.

Additionally, some hiring managers  want to assess the candidate's experience with big data technologies and their ability to work with large and complex data sets. They will definitely look for any relevant work experience in the data science field, such as prior roles as a data scientist or data analyst, so be sure to highlight those and flesh out your achievements in those sections.

If your resume passes the initial review, you’ll be invited to move on to the next stage of the interview process - the phone screening.

The Recruiter phone screening process in a data scientist interview at Wayfair typically involves an initial screening call with someone from the recruiting team. This call serves as an opportunity for the company to get a general understanding of your experience and qualifications, and to determine if you are a good fit for the role.

During the phone screening, you may be asked about your experience with programming languages and tools such as Python, R, or SQL, as well as your experience with machine learning and big data technologies. The interviewer may also ask about your experience with data visualization, as well as your background in a quantitative field such as statistics, mathematics, or computer science.

It is important to be prepared to discuss your relevant experience and technical skills, and to provide examples of how you have applied them in previous projects. The interviewer may also ask behavioural questions to understand your problem-solving skills and approach to data analysis.

The goal of the phone screening is to determine if you have the technical qualifications and experience required for the role, as well as to assess your fit with the company culture. If you pass the phone screening, you will typically be invited to continue with the technical assessment.

1. Can you tell us about a time when you had to work with large datasets?
2. How do you approach data modelling and data cleaning?
3. How do you handle missing data in a dataset?
4. How do you deal with class imbalance in a dataset?
5. How do you prioritize feature selection for a machine learning model?
6. Can you walk us through a machine learning project you have worked on from start to finish?
7. How do you handle overfitting in a model?
8. Can you explain the difference between supervised and unsupervised learning?
9. Can you explain the bias-variance trade-off?
10. Have you worked with reinforcement learning algorithms? If so, can you give an example?

Throughout the technical assessment process, you will be evaluated on your technical skills, problem-solving ability, and communication skills. It is important to prepare for each stage of the process and to demonstrate your passion for data science and your potential to contribute to the team at Wayfair.

1. Technical Interview: You will have a technical interview where you will be asked questions about the full range of DS skills a candidate is expected to have.. This interview can cover a really wide range of topics - different candidates have reported being asked different topics such as data structures, algorithms, machine learning, and statistics.
2. Case Study: Candidates have reported being asked for Analytics cases and Product cases. You’ll be presented with a real world scenario or a dataset on which you need to theorize approaches or do on the spot analysis. In some ML heavy roles, candidates reported being asked to build an ML model and present their thinking to an interview panel.
3. Take-home Project: You may also be asked to complete a take-home project that is relevant to the position you are applying for. 

1. Can you walk us through a recent data science project you worked on?
2. How do you handle missing data in a dataset?
3. Can you explain the difference between supervised and unsupervised learning algorithms?
4. Have you worked with reinforcement learning algorithms? Can you give an example?
5. Can you explain the bias-variance trade-off in a machine learning model?
6. Can you discuss a time when you had to deal with class imbalance in a dataset?
7. How do you approach feature selection for a machine learning model?
8. How do you prevent overfitting in a model?
9. Can you talk about a data visualization project you have worked on?
10. Can you discuss your experience with big data technologies such as Hadoop, Spark, etc.?

The onsite interview is an important step in Wayfair's Data Scientist interview process. This stage of the interview process is typically an opportunity for the company to get a deeper understanding of your skills and experience, as well as to assess your fit for the team and the company culture.

The onsite interview typically includes several components, such as:

1. Technical Interview: You will be asked technical questions related to data science, machine learning, statistics, algorithms, and more. The questions are designed to assess your technical abilities and problem-solving skills.
2. Case Study: You may be asked to solve a data science case study on-site, using the data, tools, and resources provided. This is an opportunity to demonstrate your skills in a real-world scenario.
3. Technical Presentation: You may be asked to give a technical presentation on a data science project you have worked on in the past. This is an opportunity to showcase your skills, expertise, and approach to data science.
4. Culture Fit: The onsite interview will also include discussions about your fit for the team and the company culture. You may be asked about your values, work style, and how you approach teamwork and collaboration. Try to think of key stories and messages you want your interviewer to walk away with, in advance for this interview. We'd recommend checking out Wayfair's values and mission here - an effective mechanism can be to weave in key themes of their mission statement or values they care about, directly into your stories where relevant. They're definitely going to be part of your interviewers' scoring rubrik, so it's important to make it as easy as possible for them to understand how your past experiences map to the sort of approach they look for.

https://www.wayfair.com/about/community.php#:~:text=Our%20mission%20is%20to%20make,that%20help%20make%20a%20home.

It is important to prepare for the onsite interview by reviewing your technical skills, practicing case studies, and reflecting on your fit for the team and the company culture. It is also helpful to come with questions for the interviewers to show your interest in the role and the company.

1. Can you explain the difference between supervised and unsupervised learning algorithms?
2. Have you worked with reinforcement learning algorithms? Can you give an example?
3. Can you discuss a time when you had to deal with class imbalance in a dataset?
4. How do you approach feature selection for a machine learning model?
5. Can you talk about a data visualization project you have worked on?
6. How do you prevent overfitting in a model?
7. Can you discuss your experience with big data technologies such as Hadoop, Spark, etc.?
8. Can you walk us through your process for evaluating the performance of a machine learning model?
9. Can you explain the bias-variance trade-off in a machine learning model?
10. Can you give an example of how you have used A/B testing in a previous project?