CBRE Data Scientist Interview Questions + Guide in 2025

CBRE is the world's largest commercial real estate services and investment firm, dedicated to delivering exceptional outcomes for its clients by integrating real estate solutions with innovative data insights.

As a Data Scientist at CBRE, you will be at the forefront of leveraging complex business, operational, and customer data to enhance decision-making and optimize performance. Your key responsibilities will include developing advanced analytical models, implementing machine learning algorithms, and collaborating with cross-functional teams to provide actionable insights. The role requires proficiency in programming languages such as Python and R, along with a strong understanding of both structured and unstructured data. You will also focus on developing custom algorithms to address unique real estate challenges while ensuring data integrity and quality.

A successful candidate will demonstrate an inquisitive mindset, strong analytical skills, and the ability to communicate complex concepts clearly to various stakeholders. This position aligns with CBRE’s commitment to integrity, service, and excellence, making you a vital contributor to the company’s mission. By preparing with this guide, you will gain a deeper understanding of the expectations and competencies required for the role, allowing you to confidently navigate the interview process.

The interview process for a Data Scientist role at CBRE is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's values and expectations. The process typically unfolds in several key stages:

1. Application and Initial Contact

Candidates begin by submitting their applications through online platforms. Following this, selected candidates receive an email from a recruiter or an assistant to a senior manager, inviting them for an initial conversation. This initial contact is often friendly and serves to gauge the candidate's interest and fit for the role.

2. Technical Assessment

The next step usually involves a technical assessment, which may include a written test or a coding challenge. This assessment focuses on evaluating the candidate's proficiency in data science concepts, statistical analysis, and programming skills, particularly in Python and relevant libraries. Candidates may be asked to solve problems related to machine learning algorithms, data manipulation, and statistical metrics.

3. Video Interviews

Candidates who perform well in the technical assessment are typically invited to participate in one or more video interviews. These interviews often involve discussions with hiring managers and other team members. During this stage, candidates can expect to delve deeper into their resumes, discuss past projects, and demonstrate their problem-solving abilities through case studies or hypothetical scenarios.

4. Onsite or Final Interview

The final stage may include an onsite interview or an extended video call, where candidates meet with multiple team members, including data scientists and senior management. This round assesses both technical expertise and cultural fit within the team. Candidates may be asked to present their previous work, explain their thought processes, and engage in discussions about data-driven strategies relevant to CBRE's business objectives.

5. Feedback and Decision

After the interviews, candidates typically wait for feedback, which may take several weeks. The decision-making process involves input from all interviewers, and candidates are informed of the outcome via email.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during this process.

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

Prepare for a Multi-Round Process

The interview process at CBRE typically involves multiple rounds, including written tests, personal interviews, and video calls. Be ready to discuss your resume in detail and prepare for a business case study. Familiarize yourself with the types of statistical concepts and machine learning algorithms that may be relevant to the role, such as correlation, covariance, and k-means clustering. Practicing these concepts will help you feel more confident during the technical portions of the interview.

Showcase Your Project Experience

Expect questions about your past projects, particularly those that demonstrate your ability to manage complex data sets and develop business intelligence solutions. Be prepared to discuss a project you have completed from the ground up, detailing your role, the challenges you faced, and the outcomes. Highlight your experience with data aggregation, custom algorithm development, and any relevant technologies you used, such as Python or SQL.

Communicate Clearly and Confidently

Given the feedback from previous candidates, it’s crucial to communicate complex ideas clearly and effectively. Practice explaining your technical work in a way that is accessible to non-experts. This will not only demonstrate your expertise but also your ability to collaborate with cross-functional teams. Be prepared to tactfully correct any misconceptions during the interview, as some interviewers may not have a strong background in data science.

Emphasize Cultural Fit

CBRE values integrity, service, and excellence. During your interview, align your responses with these values. Share examples of how you have demonstrated these qualities in your previous roles. Additionally, express your enthusiasm for contributing to CBRE’s sustainability goals, especially if you have experience in environmental data analysis or corporate sustainability initiatives.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills and ability to work in a team. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Highlight instances where you have successfully collaborated with others, navigated challenges, or led projects to completion. This will help interviewers gauge your fit within the team and the broader company culture.

Follow Up Thoughtfully

After your interview, send a thoughtful follow-up email thanking your interviewers for their time. Use this opportunity to reiterate your interest in the role and briefly mention a key point from your discussion that reinforces your fit for the position. This not only shows professionalism but also keeps you top of mind as they make their decision.

By preparing thoroughly and aligning your experiences with CBRE's values and expectations, you can position yourself as a strong candidate for the Data Scientist role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at CBRE. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data science principles, particularly in the context of real estate and sustainability. Be prepared to discuss your past projects, technical expertise, and how you can contribute to CBRE's goals.

Experience and Background

1. Tell me about a project you have done from the ground up.

This question aims to understand your project management skills and your ability to execute a data science project from inception to completion.

How to Answer

Discuss the project's objectives, your role, the methodologies you used, and the outcomes. Highlight any challenges you faced and how you overcame them.

Example

“I led a project to develop a predictive model for real estate pricing. I started by gathering data from various sources, cleaning it, and then applying regression analysis to identify key factors influencing prices. The model improved our pricing strategy by 15%, significantly enhancing our competitive edge.”

Machine Learning

2. How do you determine the optimal number of clusters for k-means clustering?

This question tests your understanding of clustering algorithms and their evaluation metrics.

How to Answer

Explain the methods you would use, such as the elbow method or silhouette score, to determine the optimal number of clusters.

Example

“I typically use the elbow method to visualize the variance explained as a function of the number of clusters. I look for the point where the rate of decrease sharply changes, indicating the optimal number of clusters. Additionally, I validate the results using silhouette scores to ensure the clusters are well-defined.”

3. How do you use DFT (Discrete Fourier Transform) in data science feature engineering?

This question assesses your knowledge of signal processing techniques and their applications in data science.

How to Answer

Discuss how DFT can be used to transform time-series data into the frequency domain, which can help in identifying patterns or anomalies.

Example

“I use DFT to analyze time-series data, particularly in identifying periodic patterns. For instance, in a project analyzing energy consumption, I applied DFT to detect seasonal trends, which helped in optimizing energy usage forecasts.”

4. Can you explain the difference between supervised and unsupervised learning?

This question evaluates your foundational knowledge of machine learning paradigms.

How to Answer

Clearly define both terms and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like customer segmentation in marketing data.”

5. What is your experience with large language models (LLMs) and their applications?

This question gauges your familiarity with advanced machine learning techniques.

How to Answer

Discuss your experience with LLMs, including any specific projects or frameworks you have used.

Example

“I have worked with large language models like BERT for natural language processing tasks. In a recent project, I fine-tuned BERT to analyze customer feedback, which improved our sentiment analysis accuracy by 20%.”

Statistics & Probability

6. Explain the concepts of correlation and covariance.

This question tests your understanding of statistical measures and their implications.

How to Answer

Define both terms and explain their significance in data analysis.

Example

“Correlation measures the strength and direction of a linear relationship between two variables, while covariance indicates the direction of their joint variability. A high correlation suggests a strong relationship, which is crucial for predictive modeling.”

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

This question assesses your data preprocessing skills.

How to Answer

Discuss various techniques for handling missing data, such as imputation or removal, and when to use each.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider removing those records or using more advanced techniques like KNN imputation to preserve data integrity.”

8. What are some common model evaluation metrics you use?

This question evaluates your understanding of model performance assessment.

How to Answer

List and explain various metrics relevant to the type of models you work with.

Example

“I commonly use accuracy, precision, recall, and F1-score for classification models, while RMSE and R-squared are my go-to metrics for regression models. Each metric provides different insights into model performance, which is essential for making informed decisions.”

9. Can you explain Gaussian distribution and its significance in data science?

This question tests your knowledge of probability distributions.

How to Answer

Define Gaussian distribution and discuss its properties and applications.

Example

“Gaussian distribution, or normal distribution, is characterized by its bell-shaped curve and is significant because many statistical methods assume normality. It’s crucial in hypothesis testing and confidence interval estimation, making it a foundational concept in data science.”

10. How do you assess the quality and integrity of data sources?

This question evaluates your data validation skills.

How to Answer

Discuss the steps you take to ensure data quality, including validation techniques and tools.

Example

“I assess data quality by checking for completeness, consistency, and accuracy. I use tools like data profiling to identify anomalies and implement validation rules to ensure that incoming data meets our quality standards.”