Public Storage Data Scientist interview questions & guide 2026

Our interview process is designed to evaluate your technical proficiency, product intuition, and ability to handle ambiguous, real-world data problems. The following categories represent the core areas of focus for the Data Scientist role.

Product Sense

These questions test your ability to think like a stakeholder and apply data science to business problems.

• How many people in a city of 100,000 people pay for a storage unit? How would you estimate this?
• What variables would you need to make an accurate estimate for storage demand in a specific zip code?

Success at Public Storage requires a balanced approach. You must be technically sharp, but you must also demonstrate the ability to communicate why your technical choices matter to the business.

Role-related knowledge – You must be comfortable with the full data science lifecycle, from data collection and feature engineering to model deployment. Interviewers look for deep expertise in your past projects; be ready to defend your choice of algorithms, how you handled categorical data, and the metrics you used to evaluate success.

Problem-solving ability – Given the ambiguity of many business problems, you will be evaluated on how you structure your approach. When faced with a vague question, start by defining the objective, identifying the necessary data, and articulating your assumptions clearly before diving into the technical solution.

Communication & Influence – You will be working with stakeholders who may not have a data background. Demonstrating that you can explain complex technical concepts in simple, business-oriented terms is a key differentiator.

The hiring process for a Data Scientist at Public Storage is comprehensive and rigorous. It typically begins with a recruiter screen, followed by a deeper dive with the hiring manager to discuss your background and interest in the company. You should expect a series of technical rounds that test your coding, statistics, and product intuition.

A notable component of the process is the potential for a take-home assignment. This is used to gauge your practical ability to handle end-to-end projects, from data cleaning to model formulation. The process is designed to be thorough, and it is not uncommon for candidates to meet with various members of the team, including leadership, to ensure a strong cultural and technical fit.

The visual timeline above illustrates the typical progression from initial screening to final interviews. Use this to pace your preparation; ensure you have reviewed your core technical skills before the initial manager call, and be prepared to dedicate focused time if you are invited to complete a take-home assignment.

Technical Depth & Methodology

This area focuses on your ability to execute high-quality data science work. You will be evaluated on your rigor in feature engineering, your selection of machine learning models, and your understanding of the underlying statistics.

Be ready to go over:

• Feature engineering – How you transform raw data to improve model performance.
• Categorical data – How you encode and handle non-numeric variables.

As a Data Scientist, your primary responsibility is to drive value through data. You will spend your time cleaning and preparing large datasets, conducting exploratory analysis to uncover market trends, and building predictive models that guide investment strategies. You will not work in a vacuum; you will collaborate with cross-functional partners to translate your findings into strategic business initiatives.

You will be expected to own your projects from start to finish. This means not only building the model but also ensuring that the insights are effectively communicated to stakeholders. You will frequently present your findings to leadership, requiring you to balance technical accuracy with a clear, compelling business narrative.

Candidates are expected to possess a strong foundation in both mathematics and programming. We look for individuals who are comfortable with ambiguity and have a proven track record of delivering results.

• Must-have skills: Proficiency in SQL (including window functions), expertise in Python or R, and a strong grasp of applied statistics and A/B testing principles.
• Nice-to-have skills: Experience with geospatial data analysis, prior work in real estate or retail analytics, and a Ph.D. or Master’s degree in a quantitative field.
• Soft skills: Ability to thrive in a collaborative team, strong stakeholder management, and clear, concise communication.

Q: How long does the interview process typically take? A: The process can be extensive, often spanning several weeks due to the number of rounds and the inclusion of technical assessments. We recommend starting your preparation immediately upon receiving an invitation.

Q: What differentiates successful candidates? A: Successful candidates don't just solve the technical problem; they demonstrate a product-first mindset. They ask clarifying questions, state their assumptions, and always tie their technical solution back to the business impact.

Q: Is the take-home assignment mandatory? A: In many cases, yes. If you are asked to complete one, clarify the expectations and timeline with your recruiter. Treat it as a chance to showcase your best work.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your answers concise and impactful.
• Don't skip the basics: Even if you are an expert in machine learning, be prepared to answer fundamental questions about SQL and statistics.
• Be ready for ambiguity: Many interview questions are intentionally open-ended. Use this as an opportunity to show how you think and how you narrow down a problem.

The Data Scientist role at Public Storage offers a unique opportunity to apply advanced analytics to one of the largest real estate footprints in the country. By focusing on your technical fundamentals, honing your product intuition, and practicing how you communicate your findings, you will be well-positioned to succeed.

Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford to further refine their readiness. You have the skills and the potential to make a significant impact; stay confident, remain focused, and approach every stage of the process as an opportunity to demonstrate your expertise.

The compensation data provided above reflects typical ranges for this position, accounting for base salary and variable components. Interpret these figures as a guide, noting that total compensation can vary based on your level of experience, specific team placement, and regional market adjustments.