Interview Guide Jan 09
Jan 093-4 rounds
Airbnb is a online marketplace that can be used by people to list or rent short-term lodging. You can book vacation/apartment rentals, homestays, hostel beds etc etc. The company was founded in 2008, and is HQ'd in San Francisco. AirBnB follows the marketplace model - hosts can earn extra income by renting out their homes; travellers get to book unique properties across teh world. Last year, it was used by more than 700 million users.
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?
To apply for a job at Airbnb, visit the careers page on its website. You should be able to search and apply for roles directly. Naturally, if you know someone in the company - we'd recommend the referral route since this is just so much more effective. In both options; make sure to read the job requirements very thoroughly to avoid "gotcha" moments during the interview. It's also useful to tweak your resume a bit to align it better to the qualifications/experiences required - this just makes it more likely that you'll be invited to an interview. If you need guidance on this, Prepfully has a resume review service where AirBnB recruiters can give you feedback on your resume.
Get your resume reviewed from an expert to increase the chances of getting the interview call.→ Review Resume
Disclaimer, this section's gonna sound a bit like a job description. But then, we gathered this based on a bunch of job descriptions available and "consolidated" information alongside examples to help articulate what the role actually involves. Here's a couple of bullet points:
- As a DS at Airbnb - you'll be expected to analyze and interpret large, complex, often unstructured data sets to inform business decisions. This could involve using both statistical analysis and machine learning techniques - whatever is necessary.
- You might also be expected to maintain data pipelines to support data-driven decision making (although this will usually be supported by the Data Engineering team).
- Collaborating with cross-functional teams to identify opportunities for data-driven innovation will also be a major part of your role. This will involve working with PMs, UX folks, and Software Engineers to identify ways in which data can be used to drive business value.
- Another key aspect in some DS roles will be around building predictive models to forecast business outcomes and drive optimization. For instance, Airbnb recently hired ML Engineers focused on demand forecasting to improve their modeling.
- Finally - communicating is going to be a key part of this role. It will be essential to be able to communicate quickly and effectively; including complex analysis or visualizations. You can expect to be presenting findings to senior leadership as well as more operational stakeholders.
- 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 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:
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.
- 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
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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.
- 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.
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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
- 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.