Zillow Data Scientist Interview Questions + Guide in 2025

Zillow is revolutionizing the real estate industry, providing innovative and accessible solutions for individuals looking to buy, sell, or rent homes.

As a Data Scientist at Zillow, you'll be at the forefront of applying advanced analytics and machine learning techniques to drive insights and enhance the customer experience in real estate transactions. This role encompasses a range of responsibilities including the development and implementation of predictive models, data analysis, and experimentation to optimize various business processes. A strong emphasis on NLP and machine learning is vital, particularly in relation to projects like the Zestimate, which involves estimating home values based on complex datasets. Excellent programming skills, especially in languages like Python and SQL, are essential, as is familiarity with machine learning frameworks and libraries.

The ideal candidate will possess a robust understanding of statistical concepts, a knack for problem-solving, and the ability to communicate complex findings effectively to both technical and non-technical stakeholders. A collaborative mindset is crucial, as you'll work closely with cross-functional teams to translate data-driven insights into actionable strategies that resonate with Zillow's commitment to equity and innovation.

This guide serves as an invaluable resource for preparing for your interview, equipping you with insights into the role and the types of questions you may encounter, ultimately helping you to stand out as a candidate.

The interview process for a Data Scientist role at Zillow is structured and thorough, designed to assess both technical and behavioral competencies. Here’s a breakdown of the typical steps involved:

1. Initial Recruiter Screen

The process begins with a phone interview conducted by a recruiter, lasting about 30 minutes. This initial conversation focuses on your background, experience, and motivation for applying to Zillow. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, ensuring that both parties are aligned before moving forward.

2. Technical Assessment

Following the recruiter screen, candidates are often required to complete a take-home technical assignment. This task typically involves analyzing a dataset related to real estate, such as predicting housing prices using machine learning models. Candidates are given a week to complete this assignment, and it serves as a critical evaluation of their technical skills and ability to communicate their findings effectively.

3. Technical Phone Interview

Once the take-home assignment is submitted, candidates may participate in a technical phone interview with a hiring manager or a senior data scientist. This interview usually lasts about an hour and focuses on discussing the take-home project, as well as assessing the candidate's knowledge of machine learning concepts, statistical methods, and coding skills. Expect questions that delve into your past projects and how you approached specific data challenges.

4. Onsite Interview (or Virtual)

The final stage of the interview process is an onsite interview, which may be conducted virtually depending on circumstances. This round typically consists of multiple interviews (often five) with various team members, including data scientists and managers. Each interview lasts around 30-60 minutes and covers a mix of technical assessments, case studies, and behavioral questions. Candidates should be prepared to discuss their approach to problem-solving, data analysis techniques, and how they would apply their skills to real-world scenarios relevant to Zillow's business.

Throughout the interview process, Zillow emphasizes a collaborative and inclusive atmosphere, allowing candidates to ask questions and engage in discussions about their experiences and the company's mission.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Embrace the Collaborative Culture

Zillow is known for its welcoming and inclusive environment. During your interviews, be sure to express your enthusiasm for collaboration and teamwork. Highlight experiences where you successfully worked with others to solve complex problems or innovate solutions. This will resonate well with interviewers who value a team-oriented approach.

Prepare for Technical and Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Brush up on your machine learning fundamentals, particularly in areas relevant to real estate, such as predictive modeling and data analysis. Additionally, be ready to discuss your past projects in detail, especially those that demonstrate your ability to apply technical skills to real-world problems. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions, ensuring you convey your thought process clearly.

Showcase Your Passion for Real Estate and Technology

Zillow is at the intersection of real estate and technology, so demonstrating your passion for both is crucial. Share insights about the latest trends in real estate technology and how they can impact customer experiences. Discuss any personal projects or research that align with Zillow's mission to innovate in the real estate space, as this will show your genuine interest in the company’s goals.

Be Ready for Case Studies and Practical Assessments

Many candidates report that Zillow incorporates case studies and practical assessments into their interview process. Prepare to discuss hypothetical scenarios related to real estate data analysis or machine learning applications. Practice articulating your thought process and decision-making strategies in a conversational manner, as this will help you engage effectively with your interviewers.

Communicate Clearly and Confidently

Throughout the interview process, clear communication is key. Practice explaining complex technical concepts in simple terms, as you may need to convey your ideas to non-technical stakeholders. Additionally, be confident in your abilities and experiences; this will help you establish credibility with your interviewers.

Follow Up Thoughtfully

After your interviews, consider sending a thoughtful follow-up message to express your gratitude for the opportunity and reiterate your interest in the role. Mention specific points from the conversation that resonated with you, which can help reinforce your fit for the position and keep you top of mind for the interviewers.

By following these tips, you can position yourself as a strong candidate who not only possesses the necessary technical skills but also aligns well with Zillow's culture and mission. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Zillow. The interview process will likely assess your technical skills in machine learning, statistics, and data analysis, as well as your ability to communicate effectively and work collaboratively. Be prepared to discuss your past experiences, projects, and how you approach problem-solving in a data-driven environment.

Machine Learning

1. Explain the concept of overfitting and how you would prevent it in a model.

Understanding overfitting is crucial in machine learning, as it can lead to poor model performance on unseen data.

How to Answer

Discuss the definition of overfitting and mention techniques such as cross-validation, regularization, and pruning that can help mitigate it.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent this, I would use techniques like cross-validation to ensure the model generalizes well, apply regularization methods like L1 or L2, and consider simplifying the model if necessary.”

2. What is the purpose of pooling layers in a Convolutional Neural Network (CNN)?

This question tests your understanding of CNN architecture, which is essential for image-related tasks.

How to Answer

Explain the role of pooling layers in reducing dimensionality and computational load while retaining important features.

Example

“Pooling layers reduce the spatial dimensions of the input volume, which decreases the number of parameters and computations in the network. This helps to prevent overfitting and allows the model to focus on the most salient features.”

3. Describe a machine learning project you worked on and the challenges you faced.

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Detail the project, your role, the challenges encountered, and how you overcame them.

Example

“In a project predicting housing prices, I faced challenges with missing data. I implemented imputation techniques and feature engineering to enhance the model's performance. Ultimately, I achieved a significant improvement in prediction accuracy.”

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

This question assesses your knowledge of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and when to use them.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, precision and recall for imbalanced datasets, and F1 score for a balance between precision and recall. Additionally, I use ROC-AUC to assess the model's ability to distinguish between classes.”

5. Can you explain the difference between L1 and L2 regularization?

This question tests your understanding of regularization techniques.

How to Answer

Define both types of regularization and their effects on model training.

Example

“L1 regularization adds the absolute value of the coefficients as a penalty term to the loss function, which can lead to sparse models. L2 regularization adds the squared value of the coefficients, which tends to distribute the weights more evenly. Both help prevent overfitting but in different ways.”

Statistics & Probability

1. What is the Central Limit Theorem and why is it important?

This question assesses your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the original distribution. This is crucial for making inferences about population parameters based on sample statistics.”

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

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, including imputation and deletion.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or I may choose to delete rows or columns if the missing data is excessive and not random.”

3. Explain the concept of p-values in hypothesis testing.

This question tests your understanding of statistical significance.

How to Answer

Define p-values and their role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

4. What is the difference between Type I and Type II errors?

This question assesses your understanding of error types in hypothesis testing.

How to Answer

Define both types of errors and their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for interpreting the results of hypothesis tests.”

5. How would you explain the concept of confidence intervals?

This question evaluates your ability to communicate statistical concepts.

How to Answer

Discuss what confidence intervals represent and how they are constructed.

Example

“A confidence interval provides a range of values within which we expect the true population parameter to lie, with a certain level of confidence, typically 95%. It is constructed using the sample mean and the standard error, reflecting the uncertainty in our estimate.”

Data Analysis & Visualization

1. Describe your experience with SQL and how you use it in data analysis.

This question assesses your technical skills in data manipulation.

How to Answer

Discuss your proficiency in SQL and provide examples of how you’ve used it in past projects.

Example

“I have extensive experience with SQL for data extraction and manipulation. In a recent project, I used SQL to query large datasets, perform joins, and aggregate data to derive insights for a marketing campaign analysis.”

2. How do you approach data visualization?

This question evaluates your ability to communicate data insights effectively.

How to Answer

Discuss the tools you use and the principles of effective data visualization.

Example

“I approach data visualization by first understanding the audience and the message I want to convey. I use tools like Tableau and Matplotlib to create clear and informative visualizations, ensuring to choose the right chart types and maintain simplicity for better comprehension.”

3. Can you explain the importance of feature engineering in a machine learning project?

This question tests your understanding of the data preparation process.

How to Answer

Discuss how feature engineering impacts model performance.

Example

“Feature engineering is crucial as it transforms raw data into meaningful features that improve model performance. By creating new features or modifying existing ones, I can help the model capture underlying patterns more effectively.”

4. What tools and libraries do you prefer for data analysis?

This question assesses your familiarity with data analysis tools.

How to Answer

Mention the tools and libraries you are proficient in and why you prefer them.

Example

“I prefer using Python with libraries like Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for visualization. These tools provide a robust ecosystem for data analysis and allow for efficient handling of large datasets.”

5. How do you ensure the quality and integrity of your data?

This question evaluates your data validation practices.

How to Answer

Discuss the methods you use to validate and clean data.

Example

“I ensure data quality by implementing validation checks during data collection, performing exploratory data analysis to identify anomalies, and using techniques like outlier detection and data cleaning methods to maintain data integrity.”