Glassdoor Data Scientist Interview Questions + Guide in 2025

Glassdoor is a platform that provides transparency in the job market by offering insights into company cultures, salaries, and employee reviews, enabling users to make informed career choices.

As a Data Scientist at Glassdoor, you will be responsible for analyzing large datasets to derive actionable insights that help improve user experience and business processes. Key responsibilities include developing statistical models, conducting A/B testing, and collaborating with cross-functional teams to drive data-driven decision-making. The ideal candidate will possess strong programming skills, a solid understanding of machine learning algorithms, and the ability to communicate complex findings in a clear and effective manner. A successful Data Scientist at Glassdoor is not only technically proficient but also exhibits curiosity, critical thinking, and a passion for leveraging data to enhance the company's mission of transparency in the workplace.

This guide will help you prepare for your interview by providing insights into the expectations of the role and the types of questions you may encounter, allowing you to showcase your skills and experience effectively.

The interview process for a Data Scientist role at Glassdoor is structured to assess both technical skills and cultural fit, ensuring candidates are well-rounded and aligned with the company's values. The process typically unfolds in several key stages:

1. Initial Screening

The first step involves a brief phone call with a recruiter, lasting around 30 minutes. This conversation serves as an introduction to the role and the company, where the recruiter will discuss your background, career aspirations, and the specifics of the position. Expect an open dialogue about salary expectations, given Glassdoor's transparency in compensation.

2. Technical Screening

Following the initial screening, candidates usually participate in a technical interview with the hiring manager. This session, conducted via video call, focuses on your technical expertise and problem-solving abilities. You may be asked to discuss your previous projects, particularly those involving machine learning and data analysis. Additionally, a coding challenge or a conceptual case study may be presented to evaluate your analytical thinking and approach to real-world data problems.

3. Take-Home Assignment

Candidates are often required to complete a take-home data analysis assignment. This task typically takes a few hours and is designed to assess your practical skills in data manipulation and analysis. The assignment may involve analyzing a dataset and presenting your findings, which allows you to showcase your technical abilities and thought process.

4. Onsite Interviews

The final stage consists of an onsite interview, which can last several hours and includes multiple rounds with different team members. During these interviews, you will encounter a mix of technical and behavioral questions. Interviewers will delve into your understanding of statistical methods, machine learning concepts, and your past experiences. Expect to discuss how you would approach specific business problems and your reasoning behind various analytical techniques. The interviews are designed to be conversational, allowing you to engage with the interviewers and demonstrate your fit within the team.

Throughout the process, candidates have noted the friendly and respectful demeanor of the interviewers, contributing to a positive overall experience.

Now that you have an understanding of the interview process, let's explore the types of questions you might encounter during your interviews.

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

Understand the Interview Structure

The interview process at Glassdoor typically involves multiple rounds, including an initial HR call, technical interviews, and a final panel interview. Familiarize yourself with this structure and prepare accordingly. Expect a mix of behavioral and technical questions, as well as case studies that reflect the work you would be doing. Knowing the format will help you manage your time and energy throughout the process.

Prepare for Case Studies

Glassdoor places a strong emphasis on case studies that are more conceptual than technical. Be ready to discuss real-world scenarios and how you would approach them. Think critically about the data you would need, the methods you would use, and how you would communicate your findings to stakeholders. Practice articulating your thought process clearly and concisely, as this will be crucial during your interviews.

Brush Up on Technical Skills

While the interviews may not be heavily technical, you should still be prepared for coding questions and discussions around machine learning and statistical methods. Review key concepts in probability, regression, and classification methods, as well as basic coding challenges. Be ready to explain your past projects in detail, focusing on the methodologies you used and the impact of your work.

Emphasize Communication Skills

Glassdoor values candidates who can communicate complex ideas effectively. During your interviews, focus on how you articulate your thoughts and findings. Practice explaining your work to someone without a technical background, as this will demonstrate your ability to bridge the gap between data science and business needs. Be prepared to discuss how you would present your analysis to different stakeholders.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture. Glassdoor is known for its friendly and respectful interview environment, so approach these questions with authenticity. Reflect on your past experiences and be ready to share examples that highlight your teamwork, problem-solving abilities, and adaptability.

Show Enthusiasm for the Company

Demonstrating genuine interest in Glassdoor and its mission can set you apart from other candidates. Research the company’s values, recent initiatives, and the specific team you are applying to. Be prepared to discuss why you want to work at Glassdoor and how your skills align with their goals. This will not only show your enthusiasm but also help you determine if the company is the right fit for you.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the position and reflect on a specific topic discussed during the interview. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By following these tips and preparing thoroughly, you can approach your interview with confidence and increase your chances of success at Glassdoor. Good luck!

Experience and Background

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Glassdoor. The interview process will likely assess your technical skills in data analysis, machine learning, and statistical methods, as well as your ability to communicate complex ideas effectively. Be prepared to discuss your past experiences and how they relate to the role.

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

Discuss the definitions of both supervised and unsupervised learning, providing 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 hidden patterns or groupings, like customer segmentation in marketing.”

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

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

How to Answer

Outline the project’s objective, your role, the methods used, and the challenges encountered. Emphasize how you overcame these challenges.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset and improved our model's accuracy by 15%.”

3. What metrics would you use to evaluate a classification model?

This question tests your understanding of model evaluation.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain when to use each metric based on the context of the problem.

Example

“I would consider accuracy for balanced datasets, but for imbalanced classes, I’d focus on precision and recall. The F1 score is useful when we need a balance between precision and recall, while ROC-AUC provides insight into the model's performance across different thresholds.”

4. How do you handle overfitting in a machine learning model?

This question evaluates your knowledge of model optimization.

How to Answer

Explain techniques such as cross-validation, regularization, and pruning. Provide examples of how you have applied these techniques in past projects.

Example

“To combat overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply L1 or L2 regularization to penalize overly complex models, which helps maintain a balance between bias and variance.”

5. What is the purpose of feature engineering, and can you give an example?

This question assesses your understanding of data preprocessing.

How to Answer

Discuss the importance of feature engineering in improving model performance and provide a specific example from your experience.

Example

“Feature engineering is crucial for enhancing model accuracy. For instance, in a sales prediction model, I created a new feature representing the interaction between marketing spend and seasonality, which significantly improved our predictions.”

Statistics & Probability

1. What statistical method would you use to measure whether there was a significant proportional difference in a population?

This question tests your statistical knowledge.

How to Answer

Discuss the appropriate statistical tests, such as chi-square tests or z-tests, and explain when to use each.

Example

“I would use a chi-square test to determine if there’s a significant difference in proportions between two categorical variables. This test is suitable when we have a contingency table of observed frequencies.”

2. How would you approach a problem where application submissions are down?

This question evaluates your analytical thinking and problem-solving skills.

How to Answer

Outline a systematic approach to investigate the issue, including data collection, analysis, and potential solutions.

Example

“I would start by analyzing historical submission data to identify trends. Next, I’d segment the data by user demographics and application types to pinpoint specific areas of decline. Finally, I would gather qualitative feedback from users to understand any barriers they might be facing.”

3. Can you explain the concept of p-values and their significance?

This question assesses your understanding of hypothesis testing.

How to Answer

Define p-values and explain their role in hypothesis testing, including what they indicate about statistical significance.

Example

“A p-value represents the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

4. What are the advantages and disadvantages of different metrics for evaluating search results?

This question tests your ability to analyze and compare metrics.

How to Answer

Discuss various metrics such as precision, recall, and F1 score, and explain their strengths and weaknesses in the context of search result evaluation.

Example

“Precision is great for measuring the relevance of results, but it can be misleading if the number of relevant results is low. Recall, on the other hand, captures the completeness of results but may include irrelevant ones. The F1 score balances both, making it a robust choice for evaluation.”

5. How do you ensure the validity and reliability of your data analysis?

This question evaluates your approach to data integrity.

How to Answer

Discuss methods for validating data sources, ensuring data quality, and conducting reproducible analyses.

Example

“I ensure data validity by cross-referencing multiple sources and conducting exploratory data analysis to identify anomalies. For reliability, I document my analysis process and use version control to maintain reproducibility in my work.”