Square Inc. (now Block Inc.), a well-known fintech provider of financial services and digital payment solutions, routinely ranks among the finest IT firms to work for. As a company doing billions of transactions each month, teams at Square work with a vast amount of data, creating opportunities for data scientists to find exciting work. The average base pay of a data scientist in the US is $141,058 per year. Square’s work environment is culturally diverse, and the company values inclusion and integrity.
The data scientist interview process at Square commonly involves testing skills in Python, SQL, and algorithms. The Square interview process involves an initial phone screen, a technical interview, and on-site interviews, as is standard for most companies. However, unlike other companies, Square does not typically give take-home challenges. Let’s look at the type of interview questions asked at Square data scientist interviews.
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This article focuses on Square data scientist interview questions to help you prepare for your next Square data scientist interview.
In this article, we’ll cover:
- Sample Square Data Scientist Interview Questions and Answers
- Sample Square Data Scientist Interview Questions for Practice
- Top Square Data Scientist Interview Questions for Experienced Professionals
- FAQs on Square Data Scientist Interview Questions
Sample Square Data Scientist Interview Questions and Answers
We’ll begin with some sample Square Data Scientist interview questions and answers to get a basic idea of what to expect.
Q1. What is a recall?
The set of all positive predictions out of the total number of positive instances that helps identify misclassified positive predictions is called recall.
Q2. What do you understand by a random forest model?
The random forest model uses decision trees as the building blocks and combines multiple decision trees together to get the output of the random forest model.
Q3. What is an RNN?
RNN stands for recurrent neural network and is a type of ML algorithm that uses the ANN or artificial neural network to find patterns.
Q4. Explain selection bias.
A bias that occurs during data sampling where a sample is not representative of the population being analyzed is called a selection bias.
Q5. What is the ROC curve?
ROC stands for Receiver Operating Characteristic. The ROC curve is a plot between true positive and false positive rates that helps us find the right trade-off between the two rates for various probability thresholds of the predicted values.
Read Square Interview Questions to get fully interview-ready for Square.
Sample Square Data Scientist Interview Questions for Practice
Here are some Square data scientist interview questions. Ensure you can solve them before your interview:
- How does A/B testing work?
- How would you build a random forest model?
- Describe reinforcement learning and root cause analysis.
- What’s the difference between univariate, bivariate, and multivariate analysis?
- How do we approach logistic regression?
- What is TF/IDF vectorization?
- What are the feature selection methods used in selecting the correct variables?
- Describe dimensionality reduction and its advantages.
- State the assumptions required for linear regression.
- Explain bagging, boosting, bias-variance trade-off, and stacking in data science.
- Explain Standard Deviation and talk about its applications.
Check out these Python Data Science Interview Questions you must prepare.
Top Square Data Scientist Interview Questions for Experienced Professionals
With the basics out of the way, let’s move a step further with some advanced Square data scientist interview questions for experienced professionals:
- Which is better and why: collaborative filtering or content-based filtering?
- What happens when there’s a violation of some assumptions necessary for linear regression?
- How would you test if a new credit risk scoring model works? What data should we look at?
- How does the K-Means algorithm work? State the areas where K-Means can be applied
- Talk about the different kernel functions we can use in SVM.
- How would you detect the stationary nature of time series data?
- Calculate the accuracy of a binary classification algorithm by using its confusion matrix.
- How can we select an appropriate value of k in k-means?
- How can we deal with outliers?
- Talk in detail about your favorite ML algorithm.
- How do you identify the peaks in a time series chart with many ups and downs?
- How would you ensure you aren’t overfitting while training a model?
We hope this list of Square data scientist interview questions will help you crack your tech interview. Read 12 key preparation tips to crack an interview to prepare better.
You can also check out Microsoft Data Science Interview Questions for more practice questions.
FAQs on Square Data Scientist Interview Questions
Q1. Does Square negotiate salary?
Yes. You can negotiate with Square on salary; they typically won’t put up timeline pressure to accept the offer, which can help create leverage. However, Square has a narrower pay band for their roles.
Q2. What questions should a data scientist ask in interviews?
Questions about team size and type, general system admin or engineering requirements, nature of work: development or implementation, etc., are helpful for a data scientist to ask in an interview.
Q3. Is data science a promising career?
Data science is in high demand, with competitive salaries, perks, and great opportunities for advancement. Glassdoor and LinkedIn agree that Data Scientist is a good career choice and a great job in the US.
Q4. Is Square a good company to work for?
Square employees think so. In fact, according to the 2021 Great Place to Work Global Employee Engagement Study, 94% of Square employees say it’s a great place to work, while only 57% of typical U.S.-based company employees say so.
Q5. What type of questions are asked at Square’s behavioral interviews?
Questions asked in a Square behavioral interview can be related to strengths and weaknesses, productivity, general workplace-related situations, past experiences, work-life balance, and challenging projects. Behavioral questions on peer-to-peer and manager relationships, managing stress and pressure in the face of strict timelines, and demanding projects can also be asked.
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