Airbnb Data Scientist Interview Guide (2025) – Process, Questions, & Prep

If you’re preparing for an Airbnb data scientist interview, you’re aiming for a role at the heart of one of the most data-driven companies in the world. In 2025, Airbnb is booking over 43 million nights and experiences each quarter and serving more than 275 million users globally—all fueled by real-time analytics, machine learning, and AI innovation. You won’t just be analyzing metrics. You’ll be driving hyper-personalization, optimizing pricing in real time, and building systems that power everything from fraud detection to AI-driven guest services. This guide breaks down the exact Airbnb data scientist interview questions you can expect, so you can walk into your interviews prepared, confident, and excited to help shape the future of travel.

Role Overview & Culture

If you’re preparing for a data scientist Airbnb interview in 2025, you’re stepping into a role where your models and insights will directly influence how millions of people travel and connect. At Airbnb, data science is not a support function. It’s a core driver of product innovation, from ranking listings and detecting fraud to running large-scale A/B tests. You’ll use tools like Python, Spark, and TensorFlow while collaborating with engineers and product teams to solve real-world problems. The company’s strong data culture means your analyses won’t just sit in dashboards—they will shape strategic decisions. This guide will walk you through the types of questions you’ll face and help you prepare to thrive in this high-impact role.

Why This Role at Airbnb?

If you’re aiming for a high-impact, high-reward role in tech, the Airbnb data scientist position stands out in 2025. You’re not just joining a globally recognized brand—you’re stepping into a career-defining opportunity with total compensation often exceeding $343,000 in the US. You’ll gain equity, travel benefits, and the freedom to work from almost anywhere in the world. The Airbnb data science environment gives you ownership over major projects that affect millions of users, whether that means launching real-time pricing models or scaling fraud detection systems. You’ll collaborate with world-class teams, build at a global scale, and develop a resume that opens doors across the tech industry—all while maintaining flexibility and work-life balance.

In the Airbnb data scientist interview process, you’ll navigate four core stages designed to rigorously assess your technical depth, business acumen, and cultural fit. Each stage is crafted to evaluate your readiness to solve real-world data challenges at scale, ensuring you’re equipped to make a measurable impact from day one. Here is how it goes:

• Application Submission
• Recruiter Screen
• Technical Screen (SQL + Python / ML Quiz)
• Virtual On-Site “Data Loop”
• Feedback & Hiring Committee

Application Submission

Your first step is a resume deep-dive, where you’ll showcase your technical skills, project impact, and alignment with Airbnb’s mission. Recruiters look for hands-on experience in SQL, Python, and advanced analytics, as well as a track record of driving business outcomes through data science. Expect a motivation chat where you’ll articulate why Airbnb excites you and how your background uniquely positions you to contribute. Highlight projects involving predictive modeling, A/B testing, and product metrics—these are highly valued. Tailoring your résumé to emphasize innovation, collaboration, and measurable results will help you stand out in this competitive stage.

Recruiter Screen

If your application catches attention, you’ll move to a 30-minute recruiter screen. This conversation is both friendly and focused, exploring your motivation, technical foundation, and fit for the data scientist role. You’ll discuss your experience with large datasets, machine learning, and business impact, as well as your familiarity with Airbnb’s products and values. Recruiters want to see clear communication, curiosity, and a genuine connection to Airbnb’s mission. Be ready to share concise stories about your past projects, your approach to problem-solving, and what excites you about working at Airbnb. This is also your chance to ask about team culture and growth opportunities.

Technical Screen (SQL + Python / ML Quiz)

Next, you’ll face a 30-minute technical assessment modeled after real Airbnb data challenges. You’ll write SQL queries to extract insights from complex tables, solve Python coding problems, and answer at least one machine learning concept question. This round tests your ability to manipulate data, apply statistical reasoning, and communicate your approach under time pressure. Expect questions on joins, aggregations, hypothesis testing, and model evaluation. Demonstrating proficiency in both SQL and Python, as well as a solid grasp of ML fundamentals, is crucial. Your performance here directly reflects your readiness to tackle Airbnb’s large-scale, data-driven problems.

Virtual On-Site “Data Loop”

If you advance, you’ll enter the virtual on-site “Data Loop,” a series of four in-depth interviews. You’ll tackle a live coding round, a product sense and A/B testing case, a machine learning system design session, and a core-values behavioral interview. Each round simulates real challenges you’ll face as a data scientist at Airbnb, from building scalable models to designing experiments and presenting actionable recommendations. You’ll be evaluated on technical rigor, business sense, and your ability to communicate complex ideas to both technical and non-technical audiences. This is your opportunity to shine as a data storyteller and strategic partner, demonstrating how you’ll drive impact across teams.

Feedback & Hiring Committee

After your interviews, Airbnb’s process moves quickly. Interviewers submit detailed feedback within 24 hours, and a hiring committee reviews your performance across all rounds. The committee calibrates for role level (L4 vs. L5) and ensures a fair, unbiased decision. You’ll be assessed not just on technical merit, but also on your alignment with Airbnb’s values and your potential to contribute to the company’s growth. If successful, you’ll receive a competitive offer and join a team where your insights will help shape the future of travel and hospitality.

To succeed in this role, you need to prepare for a wide range of Airbnb data scientist interview questions that test technical depth, product thinking, and communication.

Coding / Data Manipulation Questions

You’ll face questions that evaluate your ability to extract insights using SQL, manipulate data with Python, and solve logic-heavy problems similar to what Airbnb data scientists handle daily:

1. Write a query to return the total number of bookings in the last 90 days, last 365 days, and overall

To solve this, use conditional aggregation with SUM and CASE statements to count bookings based on the check_in_date. For the last 90 days and 365 days, compare the check_in_date with the respective date ranges, and use COUNT for the total bookings.

2. Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N

To solve this, use recursion to explore all possible combinations of integers that sum to N. Start by subtracting each integer from N and recursively solve for the remaining sum. Ensure combinations are unique by limiting the integers list passed to subsequent recursive calls.

3. Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

To solve this, join the employees and departments tables to associate employees with their departments. Use a GROUP BY clause to aggregate data by department, and a HAVING clause to filter departments with at least ten employees. Calculate the percentage of employees earning over 100K using a CASE WHEN clause combined with the AVG function, and sort the results in descending order of percentage, limiting the output to the top 3 departments.

4. Find the number of possible triangles from a list of side lengths

To solve this, use the combinations function from the itertools package to generate all possible sets of three side lengths. Then, check each combination against the triangle inequality using a helper function. Count the combinations that satisfy the inequality to determine the number of possible triangles.

5. Write a query to get the number of players who played between 5 and 10 games (5 and 10 excluded), and the number of players who played 10 games or more.

To solve this, use the SUM() function combined with CASE statements to count players in each category. The first CASE checks for players with games played between 5 and 10, while the second checks for players with 10 or more games. The SUM() function aggregates these counts.

Experimentation & A/B Testing Questions

A strong understanding of airbnb data science principles, including experimental design and causal inference, is key to measuring the impact of features and campaigns:

6. How would you determine if this discount email campaign would be effective or not in terms of increasing revenue?

To determine the effectiveness of the discount email campaign, you can conduct an A/B test by splitting users into two groups: one receiving the discount email and the other not. Measure the revenue generated from each group and compare the results. Additionally, analyze metrics like email open rates, click-through rates, and conversion rates to assess the campaign’s impact.

7. How would you test whether the feature was working successfully?

To test the feature’s success, you could conduct an A/B test where one group of dashers uses the feature and another group does not. Measure key metrics such as delivery time, customer satisfaction, and dasher earnings to determine if the feature improves efficiency and meets delivery demand. Additionally, analyze historical data to compare performance before and after the feature’s implementation.

8. How do you calculate the sample size necessary for an accurate measurement?

To calculate the sample size for an accurate measurement in an A/B test, use statistical formulas that incorporate the desired significance level, power, and minimum detectable effect size. Tools like power analysis can help determine the sample size needed to detect meaningful differences between the test and control groups.

9. Given these new marketing channels, how would you design an A/B test to utilize the marketing budget in the most efficient way possible?

To design an efficient A/B test for multiple marketing channels, allocate the budget proportionally across channels based on initial assumptions or historical data. Define clear success metrics (e.g., conversion rates, ROI) and segment the audience randomly into groups exposed to different channels. Analyze the results statistically to identify the most effective channel and optimize future budget allocation accordingly.

10. Would you think there was anything fishy about the results of an A/B test with 20 different variants?

When testing 20 variants, the probability of reaching significance by chance is high, leading to potential false positives. To address this, methods like the Bonferroni correction can be applied to adjust the significance level, or the number of variants can be reduced to improve reliability.

Machine-Learning / System Design Questions

Airbnb’s ML interviews test how well you can design scalable, intelligent systems that improve recommendations, detect fraud, or automate pricing at global scale:

11. How would you design an ML system to predict the movie score based on the review text?

To design an ML system for predicting movie scores based on review text, you can preprocess the text data using techniques like tokenization, stemming, and removing stop words. Then, use NLP models such as TF-IDF or word embeddings to convert text into numerical features. Train a regression model (e.g., linear regression, random forest, or neural networks) using these features to predict scores. Evaluate the model using metrics like RMSE or MAE to ensure accuracy.

12. How would you design a distributed authentication model using facial recognition for employee management?

To design this system, start by defining functional and non-functional requirements, such as enabling remote registration, accurate time tracking, and scalability during peak usage. Use a facial recognition model like Triple Loss Networks for dynamic user enrollment and a secure database for storing face templates and logs. Integrate the system with HR and security systems, and employ model-serving platforms for scalability and orchestration tools for batch updates.

13. Design a machine learning model to classify major health issues

To solve this, define “major health issues” in collaboration with healthcare professionals to ensure medical accuracy. Choose an appropriate model based on data complexity, such as logistic regression for simpler datasets or decision trees/random forests for nuanced data. Address missing values using imputation techniques and prioritize sensitivity to false negatives to minimize risks in predictions.

14. How would you build a machine learning system to generate Spotify’s discover weekly playlist?

To generate Spotify’s Discover Weekly playlist, you can use collaborative filtering and content-based filtering techniques. Collaborative filtering analyzes user listening patterns and preferences to recommend songs based on similar users, while content-based filtering uses song metadata like genre, tempo, and artist to suggest songs. Combining these methods with user feedback and reinforcement learning can refine recommendations over time.

15. What are the logistic and softmax functions? What is the difference between the two?

The logistic function maps continuous values to probabilities between 0 and 1, making it ideal for binary classification. The softmax function generalizes this to handle multiple classes by outputting a probability distribution across all classes. Logistic regression uses the logistic function for binary classification, while softmax regression uses the softmax function for multiclass classification.

16. Assume you have a logistic model that is heavily weighted on one variable and that one variable has sample data like 50.00, 100.00, 40.00, etc….

The model would not be valid because the removal of the decimal point introduces significant errors in the independent variable, distorting the relationship between the variable and the target label. To fix the model, you can visually identify and correct errors using histograms or apply clustering techniques like expectation maximization to detect and resolve anomalies in cases with a large data range.

Behavioral & Values Questions

This round focuses on how you use data to lead, collaborate, and make decisions—while staying aligned with Airbnb’s mission and core values:

17. How comfortable are you presenting your insights?

Airbnb values clear and collaborative communication, especially in cross-functional settings. To answer, describe how you structure presentations to different audiences such as product managers, designers, or executives. Mention tools you’ve used, such as Tableau, Jupyter Notebooks, or slide decks. For example, you might explain how you used visualizations to support a pricing model recommendation or how you adapted your message for both technical and non-technical teams. Emphasize confidence, clarity, and adaptability in both in-person and remote settings.

18. Why Do You Want to Work With Us

To prepare a strong answer, research Airbnb’s recent data science initiatives, such as trust and safety modeling, personalization, or marketplace optimization. Talk about what draws you to their mission of belonging, and how your skills align with their team’s impact areas. You might mention a recent A/B testing framework Airbnb released or how their open-source data tools align with your interests. Showing genuine enthusiasm, along with knowledge of the company’s values and culture, will make your answer stand out.

19. Tell me about a time when you influenced a decision using data, even when others disagreed.

To answer this, choose a situation where your analysis contradicted an initial assumption. Describe how you gathered data, tested hypotheses, and communicated your findings clearly. Focus on how you handled pushback and worked cross-functionally to align stakeholders. For example, you might have shown that a marketing initiative wasn’t improving engagement as expected, and suggested reallocating resources to a better-performing channel.

20. Tell me about a time you had to make a tradeoff between speed and accuracy.

Discuss a situation where a decision was time-sensitive and explain how you balanced fast delivery with analytical rigor. Maybe a team needed retention metrics quickly for a board meeting, and you provided an interim model using partial data, later following up with a refined analysis. Emphasize transparency and stakeholder communication in your decision-making process.

21. Describe a project where customer impact was your top priority.

This is your chance to show how your work aligns with Airbnb’s mission. Discuss a time you prioritized the guest or host experience. For example, you might have analyzed reviews to identify common pain points in a feature and used those insights to recommend improvements. Be sure to quantify the impact where possible.

Preparing for the Airbnb data scientist interview means equipping yourself with the technical depth and business acumen that set you apart in a highly competitive field. Start by mastering Airbnb-style SQL and Python: practice LeetCode-Medium SQL problems and focus on writing vectorized Pandas code to efficiently analyze large, real-world datasets. Airbnb’s technical screens often require you to extract insights from complex booking and review tables, so fluency in advanced joins, aggregations, and window functions is essential.

Deepen your experimentation skills by running power analyses and designing robust A/B tests using public Airbnb datasets, which are widely available and reflect the scale and messiness of real production data. Practice calculating sample sizes, interpreting p-values, and explaining statistical significance in business terms—these are core to the product sense and experimentation rounds.

To stand out, build a machine learning portfolio by creating or repurposing an open-source Airbnb data scientist project. For example, you might use a project like Airbnb-Modelling to clean, analyze, and model listing data, applying neural networks or regression to predict prices or review scores. Document your process and results in Jupyter Notebooks or GitHub repos to showcase your end-to-end workflow and technical rigor.

Finally, perfect your core-values storytelling by drafting four to five STAR (Situation, Task, Action, Result) anecdotes that highlight your impact, collaboration, and alignment with Airbnb’s mission. Practice articulating how you champion belonging, innovate on behalf of users, and learn from setbacks through AI Interviewer. This holistic preparation will help you approach each stage with confidence, ready to demonstrate both your technical excellence and your fit for Airbnb’s data-driven culture.

What Is the Average Salary for an Airbnb Data Scientist?

How Many Airbnb Data Scientist Jobs Are Open Right Now?

The number of Airbnb data scientist jobs open right now is growing steadily. Airbnb is actively hiring across multiple teams to support personalization, trust and safety, pricing, and AI innovation. Candidates with strong machine learning, experimentation, and business insight skills are in high demand. Find Airbnb data scientist job posts on our website.

Does Airbnb Offer Data Scientist Internships?

Yes, Airbnb offers an Airbnb data scientist internship each year for students and early-career professionals. The Airbnb data science internship typically includes a structured loop with technical interviews, case studies, and coding challenges. Interns work on meaningful projects that contribute directly to product and data science initiatives.

A successful Airbnb data scientist interview starts with the right mindset and ends with a structured, practiced approach. With the right preparation, you’ll not only meet the bar—you’ll showcase the kind of impact Airbnb values. Whether you’re just beginning or fine-tuning your strategy, follow our Airbnb Data Science Learning Path to build core skills, read Cheng Hui’s Success Story, who landed the role, or explore our curated Airbnb Data Scientist Questions Collection to simulate the interview experience. Your next opportunity to shape the future of travel starts with the preparation you commit to today.