Airbnb Data Science Interview
Airbnb has approximately 78,277 Bookings Per Day on their platform
Airbnb had over 140 Million nights booked in 2018. The amount of data that generates is just the tip of the iceberg. Their website would have billions of clicks to hit that number in bookings. That is why, Airbnb does a good amount of A/B testing for their listings. By measuring the impact of the changes done during A/B testing, on their metrics, they ensure that every change produces positive results. This provides a personalization to the Airbnb online experience which does not happen on the current hotel websites.
Interview Process
Their interview process consists of Resume/phone screens, basic data challenge, in-house data challenge and then four in person interviews. In the basic data challenge, the team sends you some datasets and basic questions that you need to answer. The in-house data challenge is like an actual session of working with the team. This is one of the best ways to interview in my personal opinion. Final in person interviews are with business partners and core team members to check of abilities and cultural fit. Airbnb interview process is well documented here.
Important Reading
Airbnb Data Science Org is divided into tracks of Analytics, Algorithms and Inference. The ML intensity increases from Analytics to Inference. Analytics team concentrating highly on analytics and visual side of data science and Inference is high level of statistics and ML.
- A/B testing course: Udacity A/B Testing Course
- Awesome Airbnb Data Science related blog: Airbnb Blog
- Druid Platform for Realtime and Batch Analytics: Druid Platform
AI/Data Science Related Questions
- An important metric goes down, how would you dig into the causes?
- How do you remove the missing values from a data set. What if it causes bias? What will you do then?
- Design a metric that help reduce bias in the data set.
- How would you impute missing information in a dataset?
- Find the potential causes of an anomaly in web traffic dataset.
- Explain Logistic regression.
- Why are you interested in Airbnb?
- What metrics will you evaluate based on a scenario? — (e.g. Launch in new city)
- How can you report the statistical intensive results to a non-statistician group?
- Talk about your first interaction with Airbnb.
Reflecting on the Questions
The data science team at Airbnb is one of the best teams in the industry to work for. The team is extremely talented with their own in house platform custom build for Airbnb. The amount of Data is insane and it is truly a place where a Data Scientist can grow his career and also learn cross skills from different tracks. A great amount of hard work in preparing for the interviews can surely land you in a job at the future of travel experiences.
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The sole motivation of this blog article is to learn about Airbnb and its technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.
