Square Data Science Interview Questions
Square gross payment volume was 84.65 Billion U.S. dollars in 2018.
Square is a mobile payment company focused on credit card processing and merchant solutions. The company was founded in 2009 by Jack Dorsey — who is also Twitter’s co-founder and CEO — and Jim McKelvey, and aims to make commerce easy. Over the years, square has developed both software and hardware products, as well as business solutions to enable commerce. Square’s annual net revenue reached the 3.3 billion U.S. dollars mark in 2018, up from over 200 million U.S. dollars in 2012. Square internally has hardware, software, cash app, caviar, capital, risk and security as different teams working on technology. Data Science weaves through all these teams in different capacity. Square does billions of transactions every month and hence, each team has a huge amount of data which they can employ to generate interesting insights. Data Scientists from different domains and within fintech can find interesting work at Square.
The interview process for engineering and data science follows pair programming. This answer on quora provides a good view into the Data Science teams at Square. The first step is a coding screen or a probability session. It contains writing some basic Python code in a screen sharing environment with someone from the team or answering probability based questions. That is followed by on-site interviews. The first two on-site interviews are pair programming. First one might be coding and second one on data exploration. They are followed by whiteboard interviews which consists of ML, analytics, statistics and team fit.
- Caviar Recommendation Algorithm: Recommendation Platform
- Square Support Center Articles: Inferring Label Hierarchies with hLDA
- Speed vs. customizability: Comparing Two Forward Feature Selection Algorithms
Data Science Related Interview Questions
- How do you test whether a new credit risk scoring model works? What data would you look at?
- Based on an graph drawn during the interview, what do you expect the plot of average revenue per user would look like?
- Give a list of strings, find the mapping from 1–26 for each string that maximize the value for each string. Do not distinguish between capital letter and lower case, other characters do not count.
- Explain your favourite ML Algorithm in detail
- Consider a time series chart with a lot of ups and downs. How would you identify the peaks?
- What are the different places where K-Means can be applied within Square?
- Given an existing set of purchases, how do you predict the next item to purchase of a specific item?
- How do you make sure you are not overfitting while training a model?
- How does K-Means Algorithm work?
- Explain Standard Deviation and its applications.
Reflecting on the Questions
The data science team at Square publishes articles regularly on the Squareup blog. This is an example of a modern day fintech company which does billions of transactions. The questions are geared towards how important algorithms and concepts can be applied within Square. A good creative eye for Data Science application can surely land you a job with the world’s largest retail financial transactional platform!
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The sole motivation of this blog article is to learn about Square 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.