Realtor.com Interview Guide Overview for Data Scientist Position

Realtor.com is a leading online real estate platform dedicated to making buying, selling, renting, and living in homes easier and more rewarding. With a mission to empower consumers with comprehensive and accurate real estate listings and engaging tools, Realtor.com remains at the forefront of the industry.

As a Data Scientist at Realtor.com, your role will be pivotal to the company's ambitious growth strategy. You'll collaborate with marketing, product, and engineering teams to drive consumer engagement, optimize marketing strategies, and enhance the overall consumer journey. From performing complex data analyses and A/B tests to developing predictive models and presenting your findings to senior leadership, your contributions will significantly impact the company's success.

This interview guide will walk you through the interview process, commonly asked questions, and essential tips to help you land this exciting role at Realtor.com. Let’s get started!

Submitting Your Application

The first step to joining Realtor.com is to submit a detailed application tailored to the job description. Whether you've been contacted by a Realtor.com recruiter or are applying independently, ensure that your CV reflects your relevant technical skills and experiences.

When tailoring your CV, consider incorporating specific keywords from the job posting and drafting a targeted cover letter. Be sure to emphasize pertinent skills such as data analysis, SQL, and consumer analytics, and highlight your experiences in real estate tech or related fields if applicable.

Recruiter/Hiring Manager Call Screening

Once your application is shortlisted, a recruiter from Realtor.com's Talent Acquisition Team will reach out to set up an initial phone screen. During this call, the recruiter will discuss your experience, reasons for seeking a new job, and your technical skill levels. They might also ask what you know about Realtor.com and why you want to work there.

This screening call may also include some basic behavioral questions and surface-level technical discussions. This call typically lasts about 30 minutes.

Technical Virtual Interview

If you pass the initial screening, you’ll be invited to a technical interview usually conducted virtually. This stage will delve deeper into your technical expertise. Expect questions on SQL, Bayesian analysis, regression techniques, and A/B testing fundamentals.

For roles such as Senior Data Scientist or Data Scientist, you might be given take-home assignments focusing on product metrics, analytics, and data visualization. Proficiency in Python or R and advanced analytic disciplines, such as forecasting and modeling, might also be assessed.

Onsite Interview Rounds

Candidates who make it through the technical rounds are invited for onsite interviews. This comprehensive assessment will involve multiple interview sessions with various team members, including those from Product, Design, and Engineering departments.

You might face case studies, real-scenario problems, and deeper technical evaluations, including the presentation of take-home assignments. These sessions aim to gauge your ability to translate business problems into analytical frameworks, design multivariate tests, and communicate insights effectively to both technical and non-technical stakeholders.

Quick Tips for Realtor.com Data Scientist Interviews

• Know Your Tools: Familiarize yourself with digital tools relevant to the role. Experience with Omniture, Google Analytics, Amplitude, SQL, Python, and data visualization tools like Tableau or Power BI can set you apart.
• Understand the Industry: Realtor.com values experience within real estate tech, consumer search, and marketplaces. Demonstrating industry knowledge and familiarity with consumer funnels and segmentation can enhance your candidacy.
• Communicate Effectively: Excellent communication skills are crucial. You should be prepared to present your findings clearly and concisely to various stakeholders, including top-level executives. Effective communication of complex ideas to non-technical audiences is key.

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

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background. Use relatable examples to illustrate its significance in hypothesis testing.

• How should you handle a right-skewed distribution when predicting real estate home prices? When building a model to predict real estate home prices in a city, you notice the home values are skewed to the right. Determine if any action is needed and specify what steps should be taken to address the skewness.

• Bonus: How should you handle a left-skewed target distribution? If the target distribution is heavily left-skewed, describe the steps you would take to address this issue in your predictive model.

• What considerations should be made when testing hundreds of hypotheses with many t-tests? When testing hundreds of hypotheses using multiple t-tests, consider the risk of Type I errors (false positives) due to multiple comparisons. Implement corrections like the Bonferroni correction or False Discovery Rate (FDR) to control for these errors.

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and aggregates their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• How do we handle missing square footage data in Seattle housing price predictions? You have 100K sold listings with 20% missing square footage data. Describe methods to handle the missing data, such as imputation techniques or using models that can handle missing values directly.

• How would you combat overfitting in tree-based models? When training a classification model, explain strategies to prevent overfitting in tree-based models, such as pruning, using ensemble methods like random forests, or applying cross-validation.

• Does increasing the number of trees in a random forest always increase accuracy? Discuss whether sequentially increasing the number of trees in a random forest will continue to improve model accuracy, considering factors like diminishing returns and overfitting.

• How would you implement k-means clustering in Python from scratch? Given a two-dimensional NumPy array data_points, number of clusters k, and initial centroids initial_centroids, describe how to implement the k-means clustering algorithm. Return a list of cluster assignments for each data point.

• 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.

• Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a SQL query to select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K. Given employees and departments tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

• Create a function traverse_count to determine the number of paths in an (n \times n) grid. Given an integer (n), write a function traverse_count to determine the number of paths from the top left corner of an (n \times n) grid to the bottom right. You may only move right or down.

• Develop a function is_subsequence to check if one string is a subsequence of another. Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2. A subsequence is a sequence that can be derived from another sequence by deleting some elements without changing the order of the remaining elements.

What is the average salary for a Data Scientist at Realtor.Com?

Q: What is the interview process for a Data Scientist position at Realtor.com? A: The interview process at Realtor.com includes an initial recruiter interview, a technical phone screen covering topics like Bayesian statistics, SQL, and regression, followed by an onsite interview. The process is designed to be relaxed and is typically completed within two weeks.

Q: What kind of projects will I work on as a Data Scientist at Realtor.com? A: You will work on a variety of impactful projects, such as driving consumer engagement, optimizing consumer journeys, and improving product offerings through advanced analytics. You'll be involved in decision support, A/B testing, exploratory analyses, and developing KPIs for performance management.

Q: What skills are required for a Data Scientist at Realtor.com? A: Key skills include strong analytical and problem-solving abilities, proficiency in SQL, experience with web and product analytics tools (like Google Analytics and Omniture), and familiarity with statistical methods and A/B testing. Experience with Python or R, and a background in real estate tech, are also highly valued.

Q: What is the company culture like at Realtor.com? A: Realtor.com prides itself on a warm, welcoming, and inclusive culture. The company values creativity, innovation, and in-person collaboration. You'll be part of a diverse team of experts who use leading-edge technology to make a meaningful impact in the real estate industry.

Q: How does Realtor.com support professional growth and development? A: Realtor.com offers a collaborative environment where you will partner with senior leadership and present findings to executives. The company provides intellectual challenges and development opportunities to help you grow, along with a commitment to diversity and inclusion to ensure all employees can bring their full selves to work.

Conclusion

Joining Realtor.com as a Data Scientist is not just about a new job—it's about making a profound impact in the ever-evolving world of real estate! As we continue to lead the industry with comprehensive listings and innovative consumer experiences, we are seeking passionate, analytical minds to drive that mission forward. Whether it's optimizing consumer journeys through deep data insights or enhancing marketing strategies with predictive models, every role here is pivotal to our strategic growth.

The interview process at Realtor.com is as dynamic and engaging as the role itself. From a seamless recruiter interview to a technically enriching phone screen and an onsite finale, you'll find the journey both stimulating and fulfilling. Your skills in SQL, A/B testing, consumer analytics, and advanced data modeling will be your allies as you contribute to high-impact projects and strategic decisions.

Realtor.com offers not just a platform for your professional development but a collaborative and inclusive environment where your innovations can flourish and directly help millions of people find their way home. Our diverse team, competitive compensation packages, and focus on in-person collaboration ensure that you're not just building a career—you're building a better world of real estate.

Take the leap today, and let’s change the world of real estate, one home at a time. Good luck with your interview, and we look forward to seeing you innovate with us at Realtor.com!

Do the best work of your life at Realtor.com.