Expedia Group Data Scientist Interview Guide 2026

What is a Data Scientist at Expedia Group?

The role of a Data Scientist at Expedia Group is pivotal in shaping the future of travel through data-driven insights and strategies. As a Data Scientist, you will be tasked with analyzing vast amounts of travel-related data to uncover trends, optimize user experiences, and enhance operational efficiency. Your work will directly influence product development, marketing strategies, and customer engagement, making it an essential component of Expedia Group's mission to transform the way people experience travel.

In this role, you will collaborate with cross-functional teams, including engineering, product management, and business analysts, to address complex problems and deliver actionable recommendations. The data science team at Expedia Group is involved in exciting projects, such as developing predictive models for customer behavior, optimizing pricing strategies, and enhancing personalization features across various platforms. This position offers the opportunity to work on high-impact projects that not only improve user satisfaction but also drive significant business growth.

Expect to engage with intricate datasets and employ advanced analytical techniques, including machine learning, statistical analysis, and data visualization. This role is both challenging and rewarding, providing a unique opportunity to make a tangible impact in a fast-paced and innovative environment.

Common Interview Questions

During your interviews for the Data Scientist position at Expedia Group, you can expect a range of questions that assess your technical expertise, problem-solving capabilities, and cultural fit. The questions below are representative of what past candidates have encountered and are designed to illustrate common themes rather than serve as a memorization tool.

Technical / Domain Questions

These questions evaluate your foundational knowledge and technical skills relevant to data science.

Explain the difference between supervised and unsupervised learning.
What is the purpose of cross-validation in model training?
Describe a time when you applied statistical methods to solve a business problem.
How do you handle missing data in a dataset?
What metrics do you use to evaluate the performance of a classification model?

Behavioral / Leadership

These questions assess your interpersonal skills, teamwork, and alignment with company values.

Tell me about a time you faced a significant challenge in a project and how you overcame it.
Describe a situation where you had to influence a team decision.
How do you prioritize tasks when working on multiple projects?
Give an example of how you handled a disagreement with a colleague or stakeholder.
What motivates you to work in data science?

Problem-Solving / Case Studies

Expect to analyze real-world scenarios and present your thought process.

You have a dataset with user travel preferences. How would you approach segmenting the users?
If tasked with improving a specific feature of the Expedia app, what data would you analyze and why?
Describe your process for conducting an A/B test to evaluate a new feature.
How would you approach a situation where your analysis contradicts the business intuition of senior management?
Given a dataset of flight prices, how would you model price prediction for better customer recommendations?

Coding / Algorithms

You may be asked to demonstrate coding proficiency and algorithmic thinking.

Write a function in Python to calculate the mean and median of a list of numbers.
How would you implement a decision tree algorithm from scratch?
Explain the time complexity of common sorting algorithms.
Given a dataset, how would you implement a linear regression model using Python libraries?
Create a SQL query to extract user booking data for analysis.

See every interview question for this role

Practice questions from our question bank

Curated questions for Expedia Group from real interviews. Click any question to practice and review the answer.

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

NLP

Compare TF-IDF and Embeddings

Compare TF-IDF and word embeddings for short news text classification, and explain trade-offs in semantics, interpretability, and performance.

TF-IDF

Word Embeddings

Text Classification

Medium

Statistics & Probability

Understanding Type I and Type II Errors in Testing

Differentiate between Type I and Type II errors in hypothesis testing with a practical example.

Hypothesis Testing

P-Values

Statistical Significance

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

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Getting Ready for Your Interviews

Preparing for your interview at Expedia Group requires a strategic approach that encompasses both technical and behavioral aspects. To excel, you should focus on understanding the evaluation criteria that interviewers will use to assess your fit for the Data Scientist position.

Role-related knowledge – This criterion assesses your proficiency in data science concepts, tools, and methodologies. You should be well-versed in statistical analysis, machine learning algorithms, and data manipulation techniques. Demonstrating expertise through relevant projects and experiences will be crucial.

Problem-solving ability – Interviewers will evaluate how you approach complex challenges and structure your analyses. Be prepared to articulate your thought process clearly and logically, showcasing your analytical skills through case studies or practical examples.

Leadership – As a Data Scientist, you will often work in collaborative settings. Your ability to communicate effectively, influence stakeholders, and demonstrate teamwork will be assessed through behavioral questions. Highlight instances where you took initiative or led a project.

Culture fit / values – Understanding and aligning with Expedia Group's values is essential. Interviewers will seek candidates who resonate with the company's mission and can navigate ambiguity while fostering a positive team environment.

Interview Process Overview

The interview process for the Data Scientist position at Expedia Group typically involves multiple stages, designed to assess both your technical capabilities and cultural fit. Initially, candidates may go through a recruiter screening, followed by technical assessments that test your data science knowledge and coding skills.

The process often culminates in a series of panel interviews where you will engage with various team members. These interviews may involve case studies, coding challenges, and behavioral questions. It is important to note that some candidates have reported disorganization in scheduling and communication, so staying proactive about follow-ups can be beneficial.

This visual timeline outlines the typical stages of the interview process, from initial screenings to final interviews. Use it to prepare and manage your time effectively, ensuring you allocate sufficient practice for each stage.

Deep Dive into Evaluation Areas

Technical Proficiency

Technical proficiency is critical for a Data Scientist at Expedia Group. Interviewers assess your knowledge of data science tools, programming languages (such as Python and SQL), and statistical methods. Strong candidates should demonstrate hands-on experience through projects and articulate their approaches to solving data-related problems.

Statistical Analysis – Knowledge of statistical concepts, including hypothesis testing and regression analysis, is essential.
Machine Learning – Familiarity with machine learning algorithms and frameworks, such as TensorFlow or Scikit-learn, is crucial.
Data Manipulation – Proficiency in data wrangling using libraries like Pandas and NumPy is expected.

Problem-Solving and Analytical Thinking

This area evaluates how you approach and structure data analysis challenges. Candidates should be prepared to discuss their methodologies in detail, highlighting their logical reasoning and analytical skills.

Case Studies – Be ready to walk through your thought process on real-world problems and how you derived insights.
Data-Driven Decision Making – Discuss how you use data to inform business strategies and recommendations.

Communication and Collaboration

As a Data Scientist, you will frequently collaborate with cross-functional teams. Interviewers will assess how effectively you communicate your findings and work with others to drive projects forward.

Presentation Skills – Be prepared to present your analyses clearly, using visual aids when necessary.
Stakeholder Engagement – Discuss how you have managed relationships and communicated technical concepts to non-technical stakeholders.

Advanced Data Science Concepts

Having knowledge of advanced topics can set you apart. While not always covered, familiarity with these areas can enhance your candidacy.

Deep Learning – Understanding neural networks and their applications may be beneficial.
Natural Language Processing – Experience with text data analysis and sentiment modeling can be a plus.
Big Data Technologies – Knowledge of tools like Hadoop or Spark demonstrates a broader understanding of data ecosystems.
"Describe a time you developed a predictive model and its impact."
"How would you approach feature engineering for a new dataset?"
"Explain the concept of overfitting and how to mitigate it."

Key Responsibilities

As a Data Scientist at Expedia Group, your day-to-day responsibilities will encompass a variety of tasks that directly impact the company’s operations and strategic direction. You will be involved in analyzing data to derive insights that will inform product decisions, marketing strategies, and operational efficiencies.

Your responsibilities will include:

Conducting exploratory data analysis to identify trends and patterns within datasets.
Developing and validating predictive models to enhance user engagement and conversion rates.
Collaborating with product managers and engineers to design experiments and A/B tests.
Presenting findings and recommendations to stakeholders and contributing to data-driven decision-making processes.
Continuously monitoring and improving models post-deployment to ensure accuracy and relevance.

In this role, you will work closely with other teams, including engineering and marketing, to align data insights with business objectives. Expect to engage in projects that enhance user experiences across various platforms, making your contributions vital to the success of Expedia Group.

Role Requirements & Qualifications

A strong Data Scientist candidate at Expedia Group should possess a blend of technical skills, experience, and soft skills that align with the company's needs.

Must-have skills:
- Proficiency in programming languages such as Python and SQL.
- Experience with machine learning algorithms and statistical analysis.
- Strong data visualization skills using tools like Tableau or Matplotlib.
- Familiarity with cloud platforms like AWS or Google Cloud.
Nice-to-have skills:
- Background in deep learning or natural language processing.
- Experience with big data technologies (e.g., Hadoop, Spark).
- Knowledge of data engineering principles.

Frequently Asked Questions

Q: How difficult is the interview process? The interview process for the Data Scientist position at Expedia Group can be challenging, with a mix of technical assessments and behavioral questions. Candidates should expect to dedicate significant time for preparation, particularly in technical areas.

Q: What differentiates successful candidates? Successful candidates typically demonstrate a strong technical foundation, effective communication skills, and a proven ability to solve complex problems. Candidates who can showcase relevant project experience and align with the company’s culture tend to stand out.

Q: What is the company culture like at Expedia Group? Expedia Group fosters a collaborative and innovative culture that values data-driven decision-making. Employees are encouraged to share ideas and work across teams to drive impactful results.

Q: What is the typical timeline from the initial screen to an offer? The timeline can vary, but candidates often report a process that takes several weeks. It is not uncommon for candidates to experience delays in communication, so proactive follow-ups are advisable.

Q: Are there remote work options available? While many positions offer flexibility, candidates should inquire during the interview process about specific remote or hybrid work arrangements for the Data Scientist role.

Other General Tips

Practice Coding: Regularly practice coding challenges on platforms like LeetCode or HackerRank to sharpen your programming skills.
Understand the Business: Familiarize yourself with Expedia Group's products and services. Knowing the business context can enhance your analyses and recommendations.
Prepare for Case Studies: Be ready to discuss case studies, as they are a common part of the interview process. Structure your responses clearly, outlining your approach and conclusions.
Stay Updated: Keep abreast of the latest trends and technologies in data science and travel. This knowledge can provide valuable context during discussions.
Engage with Your Interviewers: Use your interviews as an opportunity to engage with your interviewers. Ask thoughtful questions about the team’s challenges and goals.

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Interview Guides

Expedia Group

What is a Data Scientist at Expedia Group?

Common Interview Questions

Technical / Domain Questions

Behavioral / Leadership

Problem-Solving / Case Studies

Coding / Algorithms

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Technical Proficiency

Problem-Solving and Analytical Thinking

Communication and Collaboration

Advanced Data Science Concepts

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

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Tip

Summary & Next Steps