Hopper Data Scientist Interview Questions + Guide in 2025

Hopper is a rapidly growing travel platform that leverages advanced machine learning algorithms and massive datasets to enhance the booking experience and provide innovative fintech solutions for travelers.

As a Data Scientist at Hopper, you will play a crucial role in addressing intricate data and engineering challenges that enhance the company’s ability to optimize pricing for lodging and other travel-related offerings. Your key responsibilities will include conducting thorough data analysis, developing and deploying pricing models, and supporting pricing experiments to drive strategic decisions. You will be tasked with improving existing machine learning models and ensuring these models are effectively integrated into the company's products. In this role, you will also collaborate closely with cross-functional teams, including product managers and engineers, to identify revenue optimization opportunities and implement data-driven strategies.

The ideal candidate will possess fluency in SQL and either Python or R, showcasing strong analytical and problem-solving skills. Experience in A/B testing and familiarity with large datasets are essential, as well as an ability to communicate complex technical concepts clearly to non-technical stakeholders. A proactive attitude towards identifying challenges and implementing scalable solutions will be key to your success in this role.

This guide is designed to help you prepare for your interview by highlighting the skills and experiences that are most valuable to Hopper, ensuring you can effectively demonstrate your fit for this exciting position.

The interview process for a Data Scientist role at Hopper is structured to assess both technical skills and cultural fit within the company. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and alignment with Hopper's mission.

1. Initial Phone Screen

The process begins with a phone interview, usually lasting about 30 minutes, conducted by a recruiter. This initial conversation focuses on your background, experiences, and motivations for applying to Hopper. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role, ensuring that you understand the expectations and responsibilities.

2. Data Challenge

Following the initial screen, candidates are often required to complete a take-home data challenge. This assignment is designed to simulate real-world problems that Hopper faces, allowing candidates to demonstrate their analytical skills and problem-solving abilities. The challenge typically involves tasks related to product ideation, hypothesis testing, data analysis, and modeling. Candidates are given flexibility in terms of time to complete this assignment, but it is expected to be submitted within a reasonable timeframe.

3. Technical Interview

After successfully completing the data challenge, candidates will participate in a technical interview. This round may involve discussions about the submitted assignment, where interviewers will delve into your thought process, methodologies, and the insights derived from your analysis. Expect questions that assess your proficiency in SQL, Python, or R, as well as your understanding of statistical concepts and A/B testing frameworks.

4. Onsite Interviews

Candidates who perform well in the technical interview may be invited for onsite interviews, which can include multiple rounds with different team members. These interviews often cover a mix of technical and behavioral questions, focusing on your past experiences, teamwork, and how you approach problem-solving. You may also encounter case studies or logic puzzles that test your analytical thinking and creativity.

5. Final Interview

The final stage typically involves a conversation with senior leadership or a VP. This interview is an opportunity for you to discuss your vision for the role, how you can contribute to Hopper's goals, and to ask any remaining questions you may have about the company or team dynamics. It’s also a chance for the leadership team to assess your fit within the broader company culture.

As you prepare for your interview, it’s essential to be ready for the specific questions that may arise during each stage of the process.

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

Understand the Company’s Data-Driven Culture

Hopper thrives on data and analytics, so it’s crucial to familiarize yourself with their approach to pricing and customer behavior. Research their fintech products and how they leverage data to solve travel-related issues. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company’s mission and operations.

Prepare for the Data Challenge

Expect a significant focus on data challenges during the interview process. Be ready to tackle problems related to product ideation, hypothesis testing, and modeling. Practice articulating your thought process clearly, as the interviewers will be looking for your ability to derive insights from data and communicate them effectively. Familiarize yourself with common metrics used in pricing strategies and be prepared to discuss how you would measure the success of various pricing models.

Showcase Your Technical Skills

Given the emphasis on SQL, Python, and A/B testing, ensure you are well-versed in these areas. Brush up on your SQL skills, particularly in data manipulation and analysis, as well as your ability to create and interpret complex queries. Additionally, be prepared to discuss your experience with machine learning models and how you have applied them in real-world scenarios. Highlight any projects where you have successfully implemented data-driven solutions.

Communicate Clearly and Confidently

Strong communication skills are essential for this role, especially when explaining complex technical concepts to non-technical stakeholders. Practice articulating your past experiences and projects in a way that is accessible and engaging. Use storytelling techniques to convey your contributions and the impact of your work, ensuring that you connect your technical expertise with business outcomes.

Be Ready for Behavioral Questions

Expect questions that assess your fit within Hopper’s entrepreneurial culture. Prepare to discuss instances where you took ownership of a project, demonstrated a bias for action, or thrived in a collaborative environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how your actions led to positive outcomes.

Anticipate a Prolonged Process

Candidates have noted that the interview process can be lengthy and may involve multiple rounds. Stay patient and proactive in your follow-ups. If you encounter delays, don’t hesitate to reach out for updates, but do so professionally. This will demonstrate your continued interest in the position and your ability to navigate the complexities of the hiring process.

Trust Your Instincts

Some candidates have expressed concerns about the potential for the company to use take-home assignments as a means of gathering ideas without genuine hiring intent. While it’s important to approach the process with an open mind, trust your instincts. If something feels off during your interactions, don’t hesitate to reassess your interest in the role.

By following these tips, you’ll be well-prepared to navigate the interview process at Hopper and showcase your skills as a data scientist. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Hopper. The interview process will likely focus on your ability to analyze data, develop pricing models, and communicate insights effectively. Be prepared to demonstrate your understanding of product metrics, analytics, A/B testing, and machine learning concepts.

Product Metrics

1. Can you come up with 1-3 hypotheses/ideas that could increase the percentage of shoppers who convert?

This question assesses your ability to think critically about product performance and user behavior.

How to Answer

Discuss your thought process in generating hypotheses based on user data and market trends. Highlight the importance of understanding customer needs and behaviors.

Example

“I believe that simplifying the checkout process could significantly increase conversion rates. Additionally, offering personalized recommendations based on user behavior could enhance user engagement and drive sales. I would quantify the potential impact by analyzing historical conversion data and estimating the uplift from these changes.”

2. If we build feature X, what four main KPIs would you look at to measure its health?

This question evaluates your understanding of key performance indicators relevant to product features.

How to Answer

Identify KPIs that align with the goals of the feature and the overall business objectives. Explain why each KPI is important.

Example

“For feature X, I would track conversion rate, user engagement (time spent on the feature), customer satisfaction (via surveys), and revenue generated from users interacting with the feature. These metrics will provide a comprehensive view of the feature's performance.”

3. How would you measure the impact of new pricing features?

This question tests your knowledge of pricing strategies and their effects on business performance.

How to Answer

Discuss the methodologies you would use to assess the impact, including A/B testing and statistical analysis.

Example

“I would implement an A/B test to compare the performance of the new pricing feature against the existing model. Key metrics such as revenue per user and conversion rates would be analyzed to determine the feature's effectiveness.”

4. What are the key performance indicators critical to assessing pricing performance?

This question gauges your familiarity with pricing metrics.

How to Answer

List relevant KPIs and explain their significance in pricing strategy.

Example

“Key performance indicators for pricing performance include price elasticity, revenue per user, customer acquisition cost, and churn rate. These metrics help us understand how pricing changes affect customer behavior and overall profitability.”

Analytics

1. Describe a data analysis project you have worked on. What was your approach?

This question allows you to showcase your analytical skills and project experience.

How to Answer

Outline the project scope, your methodology, and the outcomes.

Example

“I worked on a project analyzing customer churn for a subscription service. I collected data from various sources, cleaned and transformed it using SQL, and applied logistic regression to identify key factors contributing to churn. The insights led to targeted retention strategies that reduced churn by 15%.”

2. How do you ensure data accuracy and reliability in your analyses?

This question assesses your attention to detail and data quality practices.

How to Answer

Discuss the steps you take to validate data and ensure its integrity.

Example

“I implement data validation checks at multiple stages of the data pipeline, including during data collection, cleaning, and analysis. I also cross-reference data with external sources when possible to ensure accuracy.”

3. What tools and techniques do you use for data visualization?

This question evaluates your ability to communicate data insights effectively.

How to Answer

Mention specific tools and techniques you are proficient in and how you use them to convey insights.

Example

“I primarily use Tableau and Python’s Matplotlib library for data visualization. I focus on creating clear, informative dashboards that highlight key trends and insights, making it easier for stakeholders to understand the data.”

A/B Testing

1. Explain the process you follow to design an A/B test.

This question tests your understanding of experimental design.

How to Answer

Outline the steps involved in designing and executing an A/B test.

Example

“I start by defining the hypothesis and identifying the key metrics to measure success. Next, I segment the audience randomly into control and test groups. After running the test for a sufficient duration, I analyze the results using statistical methods to determine if the changes had a significant impact.”

2. How do you interpret the results of an A/B test?

This question assesses your analytical skills in evaluating test outcomes.

How to Answer

Discuss the statistical methods you use to interpret results and make decisions.

Example

“I analyze the results using statistical significance tests, such as t-tests or chi-squared tests, to determine if the observed differences are meaningful. I also consider the confidence intervals and effect sizes to understand the practical implications of the results.”

3. What challenges have you faced when running A/B tests, and how did you overcome them?

This question evaluates your problem-solving skills in a testing context.

How to Answer

Share specific challenges and the strategies you employed to address them.

Example

“One challenge I faced was low sample size, which affected the reliability of the results. To overcome this, I extended the test duration and ensured that the test was well-promoted to increase participation. This ultimately provided more robust data for analysis.”

Machine Learning

1. Describe a machine learning model you have built. What was the outcome?

This question allows you to demonstrate your technical expertise and project experience.

How to Answer

Detail the model, the data used, and the results achieved.

Example

“I built a predictive model using random forests to forecast customer demand for a retail product. By training the model on historical sales data, I achieved an accuracy of 85%, which helped the company optimize inventory levels and reduce stockouts.”

2. How do you approach feature selection for a machine learning model?

This question assesses your understanding of model optimization.

How to Answer

Discuss the techniques you use for selecting relevant features.

Example

“I use techniques such as recursive feature elimination and feature importance scores from tree-based models to identify the most impactful features. I also consider domain knowledge to ensure that the selected features make sense in the context of the problem.”

3. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

List the metrics you consider and explain their relevance.

Example

“I typically use metrics such as accuracy, precision, recall, and F1 score for classification models, and mean absolute error or root mean squared error for regression models. These metrics provide a comprehensive view of the model's performance and help identify areas for improvement.”