How much does data science matter to Airbnb? According to them, “Data is the voice of the customer and data science is the interpretation of the voice. Airbnb takes pride in letting the general public know about their data science and machine learning teams that power their marketplace products. A data scientist at Airbnb is someone who is well versed in handling and processing large amounts of data and interpreting it to the business functions.

A data scientist at Airbnb has the responsibility of:

  • Organizing data and creating ETL pipelines.
  • Analyzing, processing, and visualizing the data into useful information.
  • Applying data science in practical ways to move the needle.

Data scientist roles at Airbnb

At Airbnb, the roles of data scientists are divided into three tiers. Namely: analytics, inference, and algorithms.

The Data Scientist — Analytics position has to do with critiquing and asking lots of questions. Data scientists under this category have to be very detail-oriented and inquisitive while focused on analyzing data to identify business decisions to move the needle. This role is very similar to data science positions at companies like Facebook and LinkedIn.

The Data Scientist — Inference position has to do with utilizing data visualization and statistics in solving problems. Those fit for this category are candidates with vast knowledge in economics and statistics with higher degree PHD backgrounds.

The Data Scientist — Algorithms position is the most programming heavy. Data scientists are expected to work with different programming languages, create models, and deploy machine learning systems to production. The problems the data scientists have to tackle are most related to ranking recommendations and matching for all users. This role is the most similar to machine learning engineers.

The Interview Process

The Airbnb data scientist interview process consists of three phases.

Phone screening

The first phase is the recruiter phone screen. Airbnb goes through the applicant’s resume to see how qualified the candidate is. One thing that Airbnb recruiters care about for data science applicants is their knowledge of Airbnb and the products. If you can, be creative about reaching out to contact Airbnb or have a pre-analysis done on their product and think what features you would build or work on.

The Airbnb Data Science Take-Home Challenge

The second phase is the data science challenge. Airbnb will send you a data science take-home challenge. Generally, the take-home assignment or challenge is given, and they will ask you to work on it and send it back between 24 and 48 hours.

The analytics take-home challenge is a data analysis one. Given a dataset, analyze it and come up with a powerpoint presentation.

For the data scientist algorithms role, candidates are given an Airbnb take-home to challenge to solve within 3 hours. Mainly test insights from data and build a simple predictive model with reasoning for why you chose the model.

Check out the Airbnb data science take-home challenge.

On-site Interview and Presentation

The third and major phase is the in-house data challenge. At this point, the candidate is introduced to the Airbnb data team where they are shown the basics of what it is like to work at Airbnb as a data scientist. Afterwards, a real task is given to the candidate with an open ended analysis question. It is up to the candidate to sort out the data, come up with a strategy and explain to the team how their strategy would be of any use to them. The time frame for this challenge is usually 7 hours. At the expiration of the allotted time, the candidate is called upon to present his/her work to an Airbnb panel team.

Candidates who make it through this stage are now scheduled for another set of interviews. There are a total of 5 — 1:1 interviews where two are technical with white board coding and another two are product with the last one behavior.

Sample Airbnb Data Scientist Interview Questions

To help you prepare adequately for the data science interview at Airbnb, here are some of the questions you should be prepared for.

  • Design a recommender system for Airbnb listings.
  • Which tables and indexes do you need in a SQL db to manage chat threads?
  • How would you measure the effectiveness of our operations team?
  • We see a dip in page views yesterday. How would you investigate what happened?
  • How would you explain a p-value to a business person?
  • A product manager runs and AB test and comes back with a 0.04 p-value. How do you assess the validity of the result?
  • Given two tables, one containing user profile and interests, and another containing houses 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.
  • Revise the machine learning implementation of K-means and K-NN.

Last Tips

Most candidates fail out of the interview during the in-house data science take-home challenge. Coming on-site is difficult when initially getting acquainted with the schema and data system and not having enough time to work on the challenge. To prepare, warm up your fingers with datasets you can find online at Interview Query and practice other coding challenges for the algorithmic roles like LeetCode problems.

Fundamental knowledge of Machine Learning algorithm (K-means, KNN, Linear Regression, SVM, Decision Tree, Random Forest, etc.) should be known, but it doesn’t hurt to have a basic knowledge of mathematics, and probability. Basic knowledge of SWE (Software Engineering) will also come in handy.

Try the following machine learning question from an Airbnb interview on Interview Query.
Let's say we want to build a model to predict booking prices on Airbnb.
Between linear regression and random forest regression, which model would perform better and why?
Booking Regression — Interview Query machine learning problem
Let's say we want to build a model to predict booking prices on Airbnb.Between linear regression and random forest regression, which model would perform better and why?

Also, do your homework on Airbnb. Research articles on their culture, company core values, and recent products and launches.

Lastly, be ready to defend your resume when you are asked to do so. Do not take any question lightly as the recruiters do not have time for jokes. If they ask you any question, it is because they need the answer to grade your performance.