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Etsy Data Scientist Interview Guide

Interview Guide Feb 25

Are you looking for data science roles in Etsy? Here's a guide to help you!

The role of an ETSY Data Scientist

Why consider Data Scientist Role at Etsy

Etsy is an e-commerce website focused on handmade or vintage items and supplies, as well as unique factory-manufactured items. It was founded in 2005 and is headquartered in Brooklyn, New York. Etsy allows users to buy and sell goods, primarily through its online marketplace, and offers services such as direct checkout and direct deposit.

Data analysis is used to personalize the shopping experience for each user. By recommending items that are of interest to the user based on their previous purchases and browsing history, Etsy creates a more engaging and enjoyable shopping experience. Machine learning algorithms are used by Etsy to detect and prevent fraudulent activities, such as identity theft, account takeover, and false transactions. This helps keep the platform safe for both buyers and sellers. By analysing user search queries and providing relevant results, customers can easily find what they are looking for and have a better shopping experience.

In addition, data analysis helps Etsy manage its inventory by providing insights on sales trends, demand forecasting, and identifying which items are selling well and which ones need to be reordered. This ensures that the platform has the right products at the right time, helping to drive business growth. Data analytics are used to inform Etsy's marketing strategies. By identifying target demographics, measuring the effectiveness of advertising campaigns, and determining the best times to run promotions, data science helps Etsy reach the right audience at the right time.

Applying for a Data Scientist Job in Etsy

  1. Visit the Etsy careers website
  2. Prepare your application materials: You will need to submit a resume, cover letter, and any relevant project samples or portfolio. Make sure your materials are tailored to the specific position you are applying for and highlight your relevant skills and experience.
  3. Submit your application
  4. Follow up: After submitting your application, it is a good idea to follow up with a quick email or call to ensure that your application was received and to express your continued interest in the role.

ETSY Data Scientist Interview Guide

The interview process for a data scientist role at Etsy may vary depending on the specific role and the department you are applying for. However, it may typically involve the following steps:

  1. Resume and Cover Letter Review: Etsy will review your resume and cover letter to determine if you are a good fit for the role.
  2. Phone Screening: An Etsy recruiter or HR representative will conduct a phone screening to get a better understanding of your background, skills, and experience.
  3. Technical Interview: In this stage, you will be asked technical questions to assess your knowledge and skills in areas such as programming, statistics, machine learning, data analysis, and visualization.
  4. Case Study: You may be asked to solve a real-life data problem or work on a data project to demonstrate your ability to work with data and communicate your insights.
  5. On-Site Interview: The on-site interview will typically include meetings with several members of the data science team, as well as other departments such as engineering, product, and business.
  6. Reference and Background Check: If you are a finalist for the role, Etsy will check your references and conduct a background check.

Resume and Cover Letter Review

Overview

The resume and cover letter review process are the first stage of the Etsy data scientist interview process. During this stage, an Etsy HR representative or recruiter will go through your resume and cover letter to determine if you are a good fit for the role.

Here are some things they will be looking for in your resume:

  1. Relevant education and work experience: They will look for a background in data science, computer science, statistics, or a related field, and for experience working with large datasets and complex data pipelines.
  2. Technical skills: They will look for evidence of your technical skills, such as experience with programming languages like Python, R, and SQL, as well as proficiency with data analysis and visualization tools like Tableau and Jupyter Notebook.
  3. Problem-solving skills: They will look for examples of your problem-solving skills, such as a project you worked on where you used data to solve a business problem, or a difficult technical challenge you overcame.

In your cover letter, you should highlight your relevant skills and experience and explain why you are passionate about data science and why you are interested in working at Etsy. You should also show how your background and experience align with the specific requirements of the role.

It is important to tailor your resume and cover letter for the data scientist role at Etsy to increase your chances of being selected for further stages in the interview process. Make sure to proofread your materials for any typos or errors, as these can be seen as a lack of attention to detail, which is an important quality for a data scientist.

Phone Screening

Overview

The phone screening stage of the Etsy data scientist interview process is an initial conversation between you and an Etsy HR representative or recruiter. This stage is designed to determine if you are a good fit for the role and to assess your interest in the position.

Here are some things you can expect during the phone screening:

  1. Introduction: The representative will introduce themselves and the company, and give you an overview of the role and the interview process.
  2. Background and experience: The representative will ask you about your education and work experience, focusing on your relevant skills and experience in data science, computer science, or a related field.
  3. Technical knowledge: They may ask you a few technical questions to assess your knowledge of data science concepts and tools, such as programming languages, machine learning algorithms, data visualization, and data pipelines.
  4. Fit for the role: The representative will ask about your interest in data science, your career goals, and why you are interested in working at Etsy.

The phone screening is an opportunity for you to showcase your knowledge, skills, and passion for data science, and to learn more about the role and the company. It is important to prepare for the phone screening by reviewing your resume and cover letter, and by familiarizing yourself with the company and the role. Additionally, it is a good idea to have a quiet and comfortable place to take the call, and to be on time for the call.

Interview Questions

  1. Can you tell us about your background and experience in data science?
  2. How do you keep up with the latest developments in data science and technology?
  3. Can you walk us through a data project you have worked on and the impact it had?
  4. Can you explain a machine learning algorithm you have used and how it was applied in a project?
  5. Can you give an example of a complex data problem you have solved and how you approached it?
  6. Can you discuss your experience with SQL and a programming language like Python or R?
  7. Can you explain a data visualization you have created and the insights you gained from it?
  8. Can you discuss your experience working with big data technologies such as Hadoop and Spark?
  9. How do you communicate technical findings and insights to non-technical stakeholders?
  10. Can you tell us about a time when you had to work with a difficult dataset and how you overcame the challenges?

It is important to prepare for these questions and to have clear and concise answers that highlight your skills, experience, and passion for data science. You should also be prepared to ask the representative questions about the role, the company culture, and any specific projects you might be working on if hired. This is a good opportunity to show your interest in the position and to assess if it is a good fit for you.

Technical Interview

Overview

The technical interview stage of the Etsy data scientist interview process is a more in-depth conversation between you and one or more members of the data science team. This stage is designed to assess your technical skills, knowledge, and problem-solving abilities, and to give you a chance to see how you would fit in with the team and the company culture.

Here are some things you can expect during the technical interview stage:

  1. Technical questions: The interviewer will ask you a range of technical questions to assess your knowledge of data science concepts, programming languages, machine learning algorithms, data visualization, and data pipelines.
  2. Data analysis: You may be given a data analysis problem to solve, either on a whiteboard, in a Jupyter Notebook, or using a data analysis tool like Tableau. The interviewer will be looking for your ability to process, analyse, and interpret data, and to effectively communicate your insights.
  3. Technical presentation: You may be asked to give a technical presentation on a data science topic, such as a machine learning algorithm, a data visualization, or a project you have worked on. This will give the interviewer a chance to assess your technical writing and communication skills, and your ability to explain technical concepts to non-technical stakeholders.
  4. Team fit: The interviewer will also assess how you would fit in with the data science team, and how you would collaborate with other data scientists, software engineers, product managers, and business stakeholders.

It is important to prepare for the technical interview stage by reviewing your skills and experience, and by familiarizing yourself with the specific tools and technologies that the company uses. You should also have a portfolio of data science projects that you can discuss and demonstrate, and be prepared to ask the interviewer questions about the role and the company culture.

Interview Questions

  1. What is your experience with data analysis and statistics?
  2. How do you approach a data science problem?
  3. Can you explain a machine learning model you have worked with?
  4. How do you evaluate a model's performance?
  5. How do you handle missing or corrupted data?
  6. Can you walk us through a case study or project you have worked on?
  7. How do you deal with imbalanced data sets?
  8. How do you handle high dimensionality in data?
  9. Can you explain the bias-variance trade-off?
  10. Can you discuss your experience with SQL and database management?

Case Study

Overview

The case study stage of an Etsy data science interview is an opportunity for the interviewer to assess your technical and problem-solving skills. During this stage, you will be presented with a data science problem or scenario and asked to work through it in real-time. The interviewer may ask you to explain your thought process and assumptions, and may also ask you to discuss any relevant techniques or algorithms you would use to solve the problem.

Here are some tips to help you prepare for the case study stage of an Etsy data science interview:

  1. Brush up on common data science techniques and algorithms. Make sure you are familiar with basic statistical methods, machine learning models, and data visualization tools.
  2. Review real-world data science problems and how they were solved. This can give you a good sense of what types of problems you might encounter in the interview.
  3. Practice working through data science problems in real-time. You can do this by working through example problems or by participating in online data science competitions.
  4. Be clear and concise in your communication. During the case study stage, it is important to clearly articulate your thought process and assumptions, and to explain your solutions in a way that is easy for others to understand.
  5. Show your work. Do not be afraid to write out your calculations or diagram your solutions. This can help demonstrate your understanding of the problem and give the interviewer a better sense of how you approach data science problems.

Interview Questions

  1. Can you walk us through the steps you would take to solve this problem?
  2. How would you determine the most appropriate algorithm or technique to use?
  3. Can you explain how you would handle missing or corrupted data in this scenario?
  4. How would you validate your model's performance?
  5. How would you approach this problem if the data was imbalanced?
  6. Can you discuss any potential biases in the data and how you would mitigate them?
  7. How would you approach feature selection for this problem?
  8. Can you explain how you would handle a situation where the data is very high dimensional?
  9. How would you approach this problem if the data was streaming in real-time?
  10. Can you discuss any limitations or trade-offs with the solution you have proposed?

On-site Interview

Overview

The onsite stage of an Etsy data science interview is an opportunity for the company to get to know you better and assess your fit with their team and culture. During this stage, you will typically meet with several team members and participate in a series of interviews and technical assessments. The onsite stage typically lasts several hours and provides a more in-depth look at your skills, experience, and background.

Here are some tips to help you prepare for the onsite stage of an Etsy data science interview:

  1. Research the company and team you will be interviewing with. Make sure you have a good understanding of their products, services, and mission, as well as the responsibilities of the data science team.
  2. Be prepared to discuss your past experiences and projects. Be ready to provide specific examples of how you have applied data science techniques and algorithms to solve real-world problems.
  3. Brush up on your technical skills. Review the concepts and techniques you may be asked about in the technical interviews, such as machine learning models, statistical methods, and data visualization tools.
  4. Practice working through data science problems in real-time. You can do this by working through example problems or by participating in online data science competitions.
  5. Be yourself. The onsite stage is also an opportunity for the company to get to know you and assess your fit with their team and culture. Be honest, genuine, and professional in your interactions with the interviewers.
  6. Ask questions. The onsite stage is also a great opportunity for you to learn more about the company and the data science team. Be sure to ask questions that will help you determine whether the role and company are a good fit for you.

Interview Questions

  1. Can you tell us about a particularly challenging data science project you have worked on?
  2. How do you stay up-to-date with the latest developments in the field of data science?
  3. Can you walk us through your experience with data visualization and dashboarding?
  4. How do you approach collaborating with cross-functional teams, such as engineers and product managers?
  5. Can you discuss a time when you had to effectively communicate a complex data-driven insights to non-technical stakeholders?
  6. Can you give an example of a time when you had to handle a difficult data-related ethical issue?
  7. Can you discuss your experience with big data technologies, such as Hadoop and Spark?
  8. How do you prioritize and manage your workload in a fast-paced, high-pressure environment?
  9. Can you tell us about a time when you had to plan based on limited or incomplete data?
  10. Can you discuss your experience with A/B testing and experimentation design?

Reference and Background Check

Overview

The reference and background check stage of an Etsy data science interview is an important part of the hiring process. This stage typically occurs after the onsite interview, and it is designed to verify information you have provided during the interview process and gather additional information about your qualifications, skills, and background.

Here are some tips to help you prepare for the reference and background check stage of an Etsy data science interview:

  1. Be transparent about your background. Be honest about your previous work experiences and provide accurate information about your education and qualifications.
  2. Provide a list of references. Be sure to ask your references for their permission before providing their contact information to the company.
  3. Follow up with your references. Let your references know that a background check may be performed and give them a heads-up that they may receive a call or email from the company.
  4. Prepare for a background check. Be aware that a background check may include a verification of your employment history, education, and criminal history. If there is any information you are concerned about, be sure to address it proactively with the hiring manager.
  5. Keep your social media profiles professional. Be mindful of what you post on social media, as it may be reviewed during the background check process.
  6. Be respectful of the process. The reference and background check stage is a normal part of the hiring process and should not be cause for concern. Be patient and understand that it is designed to help the company make an informed hiring decision.

Interview Questions

  1. Can you confirm the dates of your previous employment and job titles?
  2. Can you provide more information about your previous job responsibilities?
  3. Can you tell us about a challenging project you worked on and how you solved the problem?
  4. Can you tell us about a time when you had to handle a difficult situation or conflict in the workplace?
  5. Can you tell us about a time when you had to take initiative to solve a problem at work?
  6. Can you provide information about any gaps in your employment history?
  7. Can you provide a reference from a previous supervisor or co-worker?
  8. Have you ever been disciplined or terminated from a previous job?

TIPS TO STAND OUT IN ETSY INTERVIEWS

  1. Show your passion for data science. Demonstrate your enthusiasm for the field and highlight how your experiences and skills align with the role and company.
  2. Be knowledgeable about the company. Research Etsy's products, services, and mission, and be prepared to discuss how your skills and experience can contribute to their goals.
  3. Be prepared to discuss your past experiences and projects. Be ready to provide specific examples of how you have applied data science techniques and algorithms to solve real-world problems.
  4. Highlight your technical skills. Be knowledgeable about the latest developments in the field and be able to discuss the techniques and algorithms you have experience with.
  5. Be able to communicate effectively. Be able to explain complex data-driven insights in a way that is accessible to non-technical stakeholders.
  6. Be a good team player. Be prepared to discuss your experience working with cross-functional teams, such as engineers and product managers.
  7. Show your problem-solving skills. Be prepared to work through real-world data science problems in real-time, and demonstrate your ability to analyze, interpret, and communicate data insights effectively.
  8. Be professional and positive. Show your positive attitude and professional demeanor during the interview process, and be respectful of the interviewers and the company.

Remember that the interview process is an opportunity for you to demonstrate your skills and passion for data science, and to show why you would be a great fit for the role and the company.

ROLES AND RESPONSIBILITY TAKEN UP BY ETSY DATA SCIENTISTS

Etsy is a leading e-commerce platform for unique and handmade items, and data science plays a crucial role in driving business growth, improving the customer experience, and making data-driven decisions across the organization. And that is where the Etsy data scientists come in.

Here are some examples of what an Etsy data scientist's responsibilities might include:

  • Etsy data scientists collect and analyze data from various sources, including user behaviour, sales trends, and market trends, to drive business decisions. This helps the company make informed decisions that drive growth and improve the customer experience.
  • The data scientists at Etsy build and deploy machine learning models to solve business problems such as personalization, fraud detection, and search optimization. This helps the platform become more efficient and improve the customer experience.
  • To help stakeholders understand complex data sets and make informed decisions, Etsy data scientists create visual representations of data. This makes it easier for non-technical team members to understand the insights generated by the data.
  • To test the effectiveness of new features, products, and marketing campaigns, Etsy data scientists design and execute experiments. This helps the company make data-driven decisions and improve the customer experience.
  • To predict future trends and help drive business decisions, Etsy data scientists create mathematical models. This helps the company make informed decisions about future products and services.
  • Etsy data scientists collaborate with product and engineering teams to design and build new features that leverage data and machine learning. This helps the platform remain competitive and improve the customer experience.
  • Etsy data scientists work with business teams to identify areas for improvement and provide data-driven insights to inform business decisions. This helps the company make data-driven decisions and improve the overall business.

In conclusion, Etsy data scientists play a critical role in driving business growth, improving the customer experience, and making data-driven decisions across the organization. They use their technical skills in data analysis, machine learning, and data visualization to help Etsy make the most of its data assets. 

SKILLS AND QUALIFICATIONS REQUIRED

We looked at more than 60 data scientist job listings on Etsy’s website and consolidated the most common requirements.

TECHNICAL REQUIREMENTS

  • If you are looking to join an Etsy data scientist role, you should have a strong foundation in programming, with experience in popular languages like Python, R, and SQL. 
  • A familiarity with data analysis and visualization tools, such as Jupyter Notebook, Tableau, and D3.js, is also required. 
  • You should have a solid understanding of machine learning algorithms and statistical modelling techniques, as well as experience working with large, complex datasets and data pipelines. 
  • Familiarity with big data technologies, such as Hadoop, Spark, and NoSQL databases, and cloud computing platforms, such as AWS, Google Cloud, and Azure, is a plus. 
  • In addition to technical skills, you should have excellent communication skills to clearly articulate your technical findings and insights to non-technical stakeholders. 

A strong problem-solving mindset and a passion for data-driven decision making are also key qualities for success in this role.

PAYSCALE

The salary for a data scientist role at Etsy can vary depending on several factors such as experience, location, and job responsibilities. As of 2022, the average base salary for a data scientist at Etsy is approximately $120,000 - $140,000 per year, according to Payscale.com. Other factors that can influence a data scientist's salary at Etsy include the size and complexity of the projects they work on, their level of education and certifications, and the demand for their skills in the job market. It is also worth noting that many companies, including Etsy, often offer a comprehensive benefits package that can include things like health insurance, retirement plans, and paid time off, which can significantly impact an employee's overall compensation.

CONCLUSION

The interview process for a data scientist role at Etsy involves several stages: Initial Screening (resume and cover letter review), Technical Screening (phone/video interview on technical skills), Case Study (take-home or onsite problem solving), Onsite Interview (combination of technical, behavioural, and situational questions with members of the data science, engineering, and/or product management teams), and a final Reference and Background Check. Good luck!