Dropbox Data Science Interview Questions
1.2 Billion files are uploaded to Dropbox everyday.
Dropbox has over 500 million users. It has users in over 200 countries and supports over 20 languages. This explains the scale of data within the company. 4000 files are edited every second on Dropbox. All this contributes to gigantic amounts of data. The syncing of data and keeping everything up to date for so many files is a daunting task in itself and Dropbox manages all this very efficiently. Another interesting thing within Dropbox is the heavy use of Python which is the language of choice when it comes to Data Science. The product itself uses Python which makes it even better when it comes to building data science applications. This is great for any Data Scientist to build on top of. Dropbox promises an ML heavy inclination for a Data Scientist which maybe very interesting for many of them.
The interview process starts with a recruiter screen. This interview goes through your resume and a chat over the phone by the recruiter to determine if you are a fit for the role. This is followed by a phone interview with the hiring manager. If you clear this interview, the next round is an onsite interview with team members. The onsite interviews might be ML heavy depending on your team and composition of the interview panel.
- Real time Asynchronous Processing: Introducing Cape
- What file you need next: Machine Learning to predict what you need
- Edit and Deploy Feature Gates: Introducing Stormcrow
Data Science Related Interview Questions
- What is a propensity model?
- How would you set up a propensity model for the SMB team looking at companies between 5–200 employees?
- How will you up-sell to a customer based on data?
- Find out which employee reports to which manager using SQL?
- How will you maintain a data metric?
- Given a table with a series of values how will you determine if there are missing values and what are those?
- Given a root directory, return all file paths grouped by duplicate files.
- Describe how MD5 algorithm works.
- From a user perspective how can you determine that the search experience is good or bad?
- How can you analyze order data to determine churn?
Reflecting on the Questions
The data science team at at Dropbox is working in two areas. One on the BI analytics space to help improve renewal rate and reduce churn by using data. The second area is to improve the product itself like trying to know what file will be accessed next. The interview questions reflect this dichotomy with Dropbox. A data scientist should decide where he would fit and interview accordingly. Deep ML knowledge or knowledge of how to improve customer retention via data can help you land a job with one of the world’s largest document database!
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The sole motivation of this blog article is to learn about Dropbox 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.