Hertz Data Scientist Interview Questions + Guide in 2025

Hertz is a global leader in car rental services, recognized for its commitment to innovation and customer satisfaction across its extensive brand portfolio.

As a Data Scientist at Hertz, you will play a crucial role in leveraging data to drive strategic decision-making and improve operational efficiency. Your responsibilities will include designing and deploying advanced data models and algorithms to tackle complex business challenges, particularly in areas like fleet optimization, customer segmentation, and predictive maintenance. You will analyze large datasets to extract actionable insights and collaborate with cross-functional teams to integrate data science solutions into business processes.

The ideal candidate will possess strong programming skills in languages such as Python or R, and have extensive experience with SQL and machine learning frameworks. You should also have a solid understanding of data visualization tools like Tableau, as well as the ability to present complex findings in a clear and impactful manner to non-technical stakeholders. A minimum of 5 years of experience in data science, ideally within the automotive or transportation sectors, is preferred.

This guide will help you prepare effectively for your interview by providing insights into the specific skills and expertise that Hertz values, enhancing your chances of making a positive impression.

The interview process for a Data Scientist role at Hertz is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and experience.

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter. This conversation usually lasts about 30-45 minutes and focuses on your background, skills, and motivations for applying to Hertz. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. Be prepared to discuss your experience with data analysis, machine learning, and any relevant projects you've worked on.

2. Technical Interview

Following the initial screen, candidates typically participate in a technical interview with a hiring manager or a senior data scientist. This round may include a mix of technical questions and problem-solving scenarios related to data science methodologies. Expect to discuss your proficiency in SQL, machine learning frameworks, and data visualization tools. You may also be asked to explain your approach to specific data challenges or projects you've completed in the past.

3. Take-Home Project Presentation

Candidates who advance to the next stage are usually required to complete a take-home project. This project is designed to showcase your analytical skills and ability to derive insights from data. Once completed, you will present your findings to a panel, which may include data scientists and other stakeholders. During this presentation, be prepared to defend your methodology and answer questions about your choices and results. This is a critical opportunity to demonstrate your communication skills and ability to translate complex data into actionable insights.

4. Final Interviews

The final stage often consists of one or two interviews with senior management or cross-functional team members. These interviews focus on behavioral questions and assess your fit within the company culture. You may be asked to elaborate on your resume, discuss your leadership experiences, and how you approach collaboration with other teams. This is also a chance for you to ask questions about the company’s vision and how the data science team contributes to its goals.

As you prepare for your interviews, consider the following questions that have been commonly asked in the process.

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

Understand the Interview Structure

The interview process at Hertz typically involves multiple rounds, starting with a phone screen by a recruiter, followed by an interview with the hiring manager. Be prepared for a third round where you may need to present a take-home project. Familiarize yourself with the format and expectations of each round, as this will help you navigate the process more smoothly.

Prepare for Technical Questions

Given the emphasis on SQL and machine learning in the role, ensure you are well-versed in these areas. Brush up on SQL queries, including joins, group by clauses, and window functions. Additionally, be ready to discuss machine learning concepts and algorithms, as well as how you have applied them in past projects. Anticipate questions that may require you to explain your thought process and the rationale behind your choices.

Showcase Your Problem-Solving Skills

Hertz values candidates who can translate complex business problems into data-driven solutions. Be prepared to discuss specific examples from your experience where you successfully identified a problem, analyzed data, and implemented a solution. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your analytical skills and the impact of your work.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, especially when presenting findings to non-technical stakeholders. Practice explaining complex technical concepts in simple terms. During the interview, focus on clarity and conciseness in your responses, and be prepared to engage in discussions that require you to articulate your insights and recommendations.

Emphasize Collaboration and Leadership

As a Senior Data Scientist, you will be expected to mentor junior team members and collaborate with cross-functional teams. Highlight your experience in leading projects, mentoring others, and working collaboratively. Share examples that demonstrate your ability to foster a culture of learning and innovation within a team setting.

Align with Company Culture

Hertz champions diversity and inclusion, so it’s important to convey your alignment with these values. Be prepared to discuss how your unique experiences and perspectives can contribute to the company’s culture. Show enthusiasm for the role and the company’s mission, and express your desire to be part of a team that values diverse viewpoints.

Follow Up Professionally

After your interviews, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the position. This not only demonstrates professionalism but also keeps you on the interviewers' radar. If you don’t receive a response, don’t hesitate to follow up again after a reasonable period.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Hertz. The interview process will likely assess your technical skills in data analysis, machine learning, and SQL, as well as your ability to communicate complex concepts effectively. Be prepared to discuss your past experiences and how they relate to the role, as well as to demonstrate your problem-solving abilities.

Machine Learning

1. Explain the difference between classification and regression.

Understanding the distinction between these two types of predictive modeling is crucial for a data scientist.

How to Answer

Clearly define both terms and provide examples of when each would be used in a business context.

Example

“Classification is used when the output variable is a category, such as ‘spam’ or ‘not spam’ in email filtering. Regression, on the other hand, is used when the output variable is a continuous value, like predicting rental prices based on various features of the vehicle.”

2. What is a Generalized Linear Model (GLM) and how does it differ from logistic regression?

This question tests your understanding of statistical modeling techniques.

How to Answer

Discuss the concept of GLMs and highlight the specific use case of logistic regression as a type of GLM.

Example

“A Generalized Linear Model is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. Logistic regression is a specific type of GLM used for binary outcomes, where the response variable is categorical.”

3. Can you describe a machine learning project you have worked on? What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

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

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I addressed this by using techniques like SMOTE to balance the dataset and improve model performance.”

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

Understanding model evaluation metrics is essential for a data scientist.

How to Answer

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

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. The F1 score is also useful as it provides a balance between precision and recall.”

5. What are some common pitfalls in machine learning projects?

This question tests your awareness of potential issues in data science projects.

How to Answer

Mention pitfalls such as overfitting, underfitting, and data leakage, and how to avoid them.

Example

“Common pitfalls include overfitting, where the model learns noise instead of the signal, and data leakage, where information from the test set is inadvertently used in training. I mitigate these by using cross-validation and ensuring a clear separation between training and test datasets.”

SQL and Data Manipulation

1. What is the difference between INNER JOIN and LEFT JOIN?

This question assesses your SQL knowledge, which is crucial for data manipulation.

How to Answer

Explain the differences in how these joins work and when to use each.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for columns from the right table.”

2. How would you write a SQL query to find the top 5 customers by total spend?

This question tests your ability to write effective SQL queries.

How to Answer

Outline the SQL query structure and the logic behind it.

Example

“I would use a SELECT statement with SUM to aggregate total spend, GROUP BY to group by customer, and ORDER BY to sort the results in descending order, limiting the output to the top 5 customers.”

3. Can you explain what a window function is and provide an example?

This question evaluates your understanding of advanced SQL concepts.

How to Answer

Define window functions and explain their use cases.

Example

“A window function performs a calculation across a set of table rows that are somehow related to the current row. For example, using ROW_NUMBER() can help rank customers based on their total spend within their respective regions.”

4. Describe a scenario where you had to optimize a slow SQL query. What steps did you take?

This question assesses your problem-solving skills in data manipulation.

How to Answer

Discuss the steps you took to identify and resolve the performance issue.

Example

“I encountered a slow query due to a lack of indexing. I analyzed the execution plan, identified missing indexes, and added them, which significantly improved the query performance.”

5. What are common data quality issues you have encountered, and how did you address them?

This question tests your experience with data integrity.

How to Answer

Mention specific data quality issues and the strategies you used to resolve them.

Example

“I often encounter issues like missing values and duplicates. I address these by implementing data validation checks during data ingestion and using techniques like imputation for missing values and deduplication processes.”