Public Storage Data Scientist Interview Questions + Guide in 2025

Public Storage is the largest owner and operator of self-storage facilities globally, known for its commitment to employee satisfaction and diversity.

As a Data Scientist at Public Storage, you will play a pivotal role in transforming extensive datasets into actionable insights that drive strategic business decisions. Your key responsibilities will include conducting thorough data analysis to uncover trends and anomalies, developing and implementing data mining techniques, and creating visual representations of data findings through dashboards and reports. You will also be responsible for building and maintaining data models that illustrate complex business processes and predicting future outcomes. This role requires strong proficiency in SQL, Python, or R for data manipulation, experience with data visualization tools like Tableau or Power BI, and excellent problem-solving skills. A collaborative mindset is essential, as you will work closely with various business teams to tailor insights to their specific needs and ensure data quality and security through effective governance practices.

This guide aims to equip you with essential knowledge and strategies to perform well during your interview for the Data Scientist role at Public Storage. By understanding the expectations and requirements of the position, you can tailor your responses and demonstrate how your skills and experiences align with the company's goals.

The interview process for a Data Scientist role at Public Storage is structured and thorough, designed to assess both technical skills and cultural fit within the organization. Here’s a breakdown of the typical steps involved:

1. Initial Screening

The process begins with a 30 to 45-minute phone interview conducted by a recruiter. This initial screening focuses on your professional background, interest in Public Storage, and alignment with the company’s values. The recruiter will also provide an overview of the role's responsibilities and expectations, ensuring you understand the key aspects of the position.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview, which may be conducted via video conferencing. This interview focuses on your analytical skills, including your proficiency in statistics, algorithms, and programming languages such as Python or SQL. Expect to discuss your experience with data analysis, data modeling, and any relevant projects that demonstrate your ability to extract insights from complex datasets.

3. Behavioral Interview

The next step often involves a behavioral interview with a hiring manager or team lead. This round assesses your problem-solving abilities, communication skills, and how you work within a team. You may be asked to provide specific examples of past experiences where you successfully navigated challenges or contributed to team projects. This is also an opportunity to showcase your understanding of Public Storage's culture and how you can contribute to it.

4. Case Study or Take-Home Assignment

Candidates may be required to complete a case study or take-home assignment that simulates real-world data challenges relevant to the role. This task typically involves analyzing a dataset, developing a model, and presenting your findings. The goal is to evaluate your analytical thinking, technical skills, and ability to communicate complex information effectively.

5. Final Interview

The final interview usually involves discussions with senior leadership or executives. This round focuses on strategic thinking and how your skills align with the company’s long-term goals. You may be asked about your vision for data science within the organization and how you can drive business value through data-driven insights.

6. Cultural Fit Assessment

In some cases, there may be an additional interview focused specifically on cultural fit. This discussion will explore your alignment with Public Storage's values, teamwork philosophy, and commitment to diversity and inclusion. It’s essential to demonstrate your understanding of the company’s mission and how you can contribute positively to the workplace environment.

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.

Understand the Company Culture

Public Storage places a strong emphasis on teamwork, innovation, and customer service excellence. Familiarize yourself with their core values and how they translate into daily operations. Be prepared to discuss how your personal values align with the company’s mission and culture. This will not only demonstrate your interest in the role but also your potential fit within the organization.

Highlight Your Analytical Skills

As a Data Scientist, your ability to analyze large datasets and derive actionable insights is crucial. Be ready to discuss specific examples from your past experiences where you successfully identified trends or solved complex problems using data analysis. Emphasize your proficiency in SQL and Python, as these are essential tools for the role. Consider preparing a few case studies or projects that showcase your analytical skills and the impact of your work.

Prepare for Behavioral Questions

Expect a mix of behavioral and situational questions during the interview process. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Focus on your leadership experience, particularly in managing teams and driving results. Be prepared to discuss how you handle challenges, motivate team members, and collaborate with cross-functional teams to achieve business objectives.

Showcase Your Technical Expertise

Given the technical nature of the role, be prepared to discuss your experience with data visualization tools like Tableau or Power BI, as well as your knowledge of data modeling and governance. You may be asked to explain your approach to building predictive models or how you ensure data quality and integrity. Brush up on relevant algorithms and statistical concepts, as these may come up in technical discussions.

Engage with Your Interviewers

Public Storage values open communication and collaboration. During your interviews, engage with your interviewers by asking insightful questions about the team dynamics, ongoing projects, and the company’s strategic goals. This not only shows your interest in the role but also helps you assess if the company is the right fit for you.

Be Ready for a Unique Interview Process

The interview process at Public Storage may include various stages, such as assessments and discussions with multiple stakeholders. Be adaptable and prepared for different formats, including technical assessments or case studies. Approach each stage with a positive attitude and a willingness to learn, as this reflects the company’s commitment to continuous improvement.

Follow Up Thoughtfully

After your interviews, send a personalized thank-you note to express your appreciation for the opportunity to interview. Mention specific points from your conversations that resonated with you, reinforcing your interest in the role and the company. This small gesture can leave a lasting impression and demonstrate your professionalism.

By following these tips, you can position yourself as a strong candidate for the Data Scientist role at Public Storage. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Public Storage. The interview process will likely focus on your analytical skills, experience with data manipulation, and ability to communicate insights effectively. Be prepared to discuss your technical expertise, problem-solving abilities, and how you can contribute to the company's strategic goals.

Data Analysis

1. Can you describe a project where you conducted in-depth analysis of a large dataset? What were your findings?

This question assesses your practical experience with data analysis and your ability to derive actionable insights from data.

How to Answer

Discuss a specific project, the dataset you worked with, the analysis techniques you employed, and the insights you gained. Highlight how these insights impacted decision-making or business outcomes.

Example

“In my previous role, I analyzed customer transaction data from the past five years to identify purchasing trends. By applying clustering techniques, I discovered distinct customer segments, which allowed the marketing team to tailor campaigns effectively, resulting in a 20% increase in engagement.”

2. What data mining techniques are you familiar with, and how have you applied them?

This question evaluates your knowledge of data mining methods and their practical applications.

How to Answer

Mention specific data mining techniques you have used, such as classification, regression, or association rule mining, and provide examples of how you applied them in real-world scenarios.

Example

“I have experience with decision trees and random forests for classification tasks. In a project aimed at predicting customer churn, I used these techniques to identify key factors influencing retention, which helped the company implement targeted retention strategies.”

3. How do you ensure the quality and integrity of the data you analyze?

This question focuses on your understanding of data governance and quality assurance practices.

How to Answer

Discuss the methods you use to validate data, such as data cleaning, normalization, and consistency checks. Emphasize the importance of data quality in your analysis.

Example

“I always start with data cleaning to remove duplicates and handle missing values. I also implement validation checks to ensure data consistency and accuracy, which is crucial for reliable analysis and reporting.”

4. Can you provide an example of a data visualization you created? What tools did you use?

This question assesses your ability to communicate findings through visualizations.

How to Answer

Describe a specific visualization project, the tools you used (e.g., Tableau, Power BI), and how the visualization helped stakeholders understand the data.

Example

“I created an interactive dashboard in Tableau to visualize sales performance across different regions. This dashboard allowed the sales team to quickly identify underperforming areas and adjust their strategies accordingly.”

5. How do you approach analyzing operational metrics?

This question evaluates your analytical mindset and familiarity with key performance indicators (KPIs).

How to Answer

Discuss your approach to identifying relevant metrics, analyzing them, and deriving insights that can drive operational improvements.

Example

“I focus on key metrics such as occupancy rates and revenue per square foot. By analyzing these metrics over time, I can identify trends and anomalies, which helps the management team make informed decisions about resource allocation and pricing strategies.”

Machine Learning

1. What machine learning algorithms are you most comfortable with, and how have you applied them?

This question assesses your knowledge of machine learning techniques and their applications.

How to Answer

Mention specific algorithms you have experience with, such as linear regression, logistic regression, or neural networks, and provide examples of projects where you applied them.

Example

“I am comfortable with both supervised and unsupervised learning algorithms. For instance, I used logistic regression to predict customer conversion rates based on historical data, which improved our targeting strategy and increased conversions by 15%.”

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

This question focuses on your understanding of model evaluation metrics and techniques.

How to Answer

Discuss the metrics you use to evaluate model performance, such as accuracy, precision, recall, F1 score, or ROC-AUC, and explain how you apply them in practice.

Example

“I typically use accuracy and F1 score for classification models. For instance, in a recent project, I evaluated a model predicting loan defaults and found that while accuracy was high, the F1 score revealed a need for improvement in handling false positives.”

3. Can you explain the concept of overfitting and how to prevent it?

This question tests your understanding of a common challenge in machine learning.

How to Answer

Define overfitting and discuss techniques you use to prevent it, such as cross-validation, regularization, or pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well to unseen data and apply regularization methods to reduce complexity.”

4. Describe a time when you had to tune hyperparameters for a model. What was your approach?

This question evaluates your experience with model optimization.

How to Answer

Discuss the specific model you tuned, the hyperparameters you adjusted, and the methods you used for tuning, such as grid search or random search.

Example

“I worked on a random forest model where I tuned hyperparameters like the number of trees and maximum depth. I used grid search with cross-validation to find the optimal settings, which improved the model’s accuracy by 10%.”

5. How do you handle missing data in your datasets?

This question assesses your data preprocessing skills.

How to Answer

Discuss the strategies you use to handle missing data, such as imputation, deletion, or using algorithms that can handle missing values.

Example

“I typically assess the extent of missing data first. For small amounts, I might use mean imputation, but for larger gaps, I prefer to analyze the data patterns and consider using algorithms that can handle missing values directly, like decision trees.”

Statistics & Probability

1. Can you explain the difference between Type I and Type II errors?

This question tests your understanding of statistical hypothesis testing.

How to Answer

Define both types of errors and provide examples to illustrate the differences.

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. For example, in a clinical trial, a Type I error might mean concluding a drug is effective when it is not, while a Type II error would mean missing a truly effective drug.”

2. How do you determine the sample size for a study?

This question evaluates your knowledge of statistical power and sample size calculations.

How to Answer

Discuss the factors that influence sample size, such as effect size, desired power, and significance level, and how you calculate it.

Example

“I consider the expected effect size, the desired power level (usually 0.8), and the significance level (typically 0.05). I use power analysis to determine the minimum sample size needed to detect a statistically significant effect.”

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

This question assesses your understanding of fundamental statistical concepts.

How to Answer

Explain the Central Limit Theorem and its implications for statistical inference.

Example

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

4. How do you interpret p-values in the context of hypothesis testing?

This question tests your understanding of statistical significance.

How to Answer

Define p-values and explain their role in hypothesis testing, including common misconceptions.

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 (typically < 0.05) suggests that we reject the null hypothesis, but it does not measure the size or importance of the effect.”

5. Can you explain the concept of confidence intervals?

This question evaluates your understanding of estimation and uncertainty.

How to Answer

Define confidence intervals and discuss their interpretation and significance in statistical analysis.

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 (e.g., 95%). It reflects the uncertainty of our estimate and is useful for understanding the precision of our results.”