Expedia Data Science Interview Questions
There are 37 million Expedia members across 32 countries.
Expedia has covered 534 billion miles in air travel, this is enough for 72 round trips (in passenger miles flown) from the sun to Pluto and back. Expedia is a travel company like Booking.com which we have covered at Acing AI previously. It has sold enough hotel room nights in the last 20 years to account for every person living in the United States. The amount of data Expedia accumulates by having so many travellers every year leads to huge investment in technology. Expedia has invested over $850M trailing year over year in tech spend. A mature tech stack helps Data Scientists at their job. This is a great opportunity for any Data Scientist to build their career.
If you are short-listed after resume screening, there is first interview with the manager of the data science team. These include technical questions about machine learning and statistics. After clearing that, there is a technical coding interview. The third round is an interview with HR more classic and typical job interview.
- Streaming Data Ecosystems: Slideshare
- Spark Streaming + Kafka Best Practices at Expedia: Slideshare
- Tips by an Insider on Data Science Interviewing: Sixty People at Expedia
Data Science Related Interview Questions
- What is the process of cross validation?
- How can we do price optimization for properties on Expedia?
- Predict Hotel prices in a given dataset.
- Explain a Machine Learning project on your resume.
- Develop a recommendation system based on a provided dataset.
- Which flight path is more profitable for London-Lisbon or London-Milan?
- Should we invest on buying more property in X city?
- Explain linear and logistic regression.
- Give pros and cons of SVM.
- Explain the meaning of overfitting to non technical people.
Reflecting on the Question
The data science team at Expedia is geographically dispersed. The technical team has build a very mature data science architecture that enables the Data Science team. The questions are based on the questions the data science team at Expedia answers day to day. Great product sense about the Expedia product and its business can surely land you a job at one of the world’s largest travel sites!
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The sole motivation of this blog article is to learn about Expedia 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.
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