Airbnb Data Scientist


Airbnb data scientist interview process

The role of an Airbnb Data Scientist

Airbnb, Inc. is an American organization that runs an online marketplace for travel-related services and accommodations, mainly homestays for holiday rentals. The platform, which has a San Francisco, California, base, is reachable through a website and a mobile app.

Interview Guide

The roles of data scientists at Airbnb are divided into three segments. Analytics, Inference, and Algorithms

  • The Data Scientist — Analytics 

This position entails providing feedback and raising several inquiries. This group of data scientists must be highly meticulous and inquisitive while concentrating on data analysis to find strategic business decisions. 

  • The Data Scientist — Inference 

In this role, problem-solving will involve using statistics and data visualization. Candidates with extensive economics and statistical understanding and backgrounds with doctoral degrees fall into this category.

  • The Data Scientist — Algorithms 

This position involves maximum programming. Data scientists must work with various computer languages, develop models, and put machine learning systems into use. Ranking suggestions and matching for all users are the issues that data scientists must most frequently deal with. Machine learning engineers are most compatible with this position.

How to Apply for the Data Scientist Job at Airbnb?

You can apply for the Airbnb Data Scientist job on the career page of But before applying, be sure to have an impressive and impactful resume for selection. You can opt for the resume review service on Prepfully to ensure you have a job-ready resume. 

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Role of a Data Scientist at Airbnb

  • Assist clients in making decisions, and provide them with data.
  • Data collection, analysis, and management of new research. 
  • Changing the format of the data to make it more suitable for analysis.
  • Assisting the BI team with data collection, analysis, and reporting.
  • Analyzing large data sets to find helpful information.
  • Reporting and creating business presentations.

Preferred Skills/Qualifications

  • Programming experience in SQL, Python, R, and Scala.
  • Work experience with business intelligence tools (for example, Tableau).
  • Skills in mathematics (such as algebra and statistics). 
  • Expertise in exploratory data analysis.
  • Working knowledge of the most common data science toolkits.
  • Statistic knowledge is essential.
  • An excellent ability to write technical documents.
  • Knowledge of how to visualize data effectively for a given study or project.
  • Working knowledge of machine learning and artificial intelligence. 
  • Knowledge of data management tools. 
  • Analytical and problem-solving capabilities.
  • Ability to operate in a team both independently and with others from different backgrounds.
  • Focusing on even the slightest of details.

The Interview process 

The interview process for the Airbnb data scientist role consists of 3 stages:

  • Phone Screen
  • Data Science-Take Home Challenge
  • Onsite Interview

Here's a more detailed description of the interview process:

Phone Screen Round


The phone interview with the recruiter is the first stage in which Airbnb reviews the resume, and the applicant's qualifications are tested. One quality that Airbnb recruiters look for in data science candidates is familiarity with Airbnb and its offerings.


Consider what features you would design or work on before contacting Airbnb.

Interview Questions

  • What metrics would you use to evaluate the performance of our operations team?
  • How would you make up for missing data?
  • Describe your background to the hiring manager 

Data Science Take-Home Challenge


The Data Science Challenge is the second stage. You will receive a data science take-home challenge from Airbnb. The take-home task or challenge has a 'to be completed and submitted' clock time of 24 to 48 hours.

Data analysis is the take-home analytics task. You must analyze a dataset and create a PowerPoint presentation using the results. In three hours, candidates for the data scientist algorithm position must complete a take-home task from Airbnb. Specifically, test data findings, create a straightforward predictive model, and justify the model selection.


  • Warm up your hands using datasets and practice additional code tasks for algorithmic roles.
  • Understanding machine learning methods like K-means, KNN, linear regression, SVM, decision trees, and random forests is essential.
  • Do some research about Airbnb. Read stories about the company's culture, fundamental principles, and most recent products and debuts.

Interview Questions

  • Given two tables, one containing user profile and interests and another containing house to be recommended, along with topic tags and metadata such as amenities, price, reviews, location, country, topic, etc. Create a recommendation engine using this data.
  • Find the average accommodates-to-beds ratio for shared rooms in each city. Sort your results by listing towns with the highest ratios first.
  • You're given a dataset of searches for properties on Airbnb. For simplicity, let's say that each search result (i.e., each row) represents a unique host. Find the city with the most amenities across all their host's properties. Output the name of the city.
  • Given a set of Airbnb data, analyze it to recommend 2-3 product changes to grow bookings.

Onsite Interview Round


The onsite data challenge is the third and most crucial stage. The candidate is now introduced to the Airbnb data team and given a brief overview. The candidate is next given an actual task and asked an open-ended analysis question. It is the candidate's responsibility to analyze the facts, develop a plan, and convince the team of the value of their plan. The typical time limit for this task is seven hours.

This round consists of interview sessions with

-Technical members for coding

-Product-oriented team members

It ends with a behavioral interview session

Interview Questions

  • Design a recommender system for Airbnb listings.
  • Which tables and indexes do you need in a SQL DB to manage chat threads?
  • We saw a dip in page views yesterday. How would you investigate what happened?
  • Write a query to find which gender gives a higher average review score when writing reviews as guests. Use the `from_type` column to identify guest reviews. Output the gender and their average review score.
  • Suggest a data solution to measure the performance of a newly introduced feature on the Airbnb platform.

Get more data scientist interview questions to prepare well for the Airbnb interview rounds.

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