Square, Inc. is a financial service, merchant services aggregator, and mobile payment company. Founded in 2009 and based in San Francisco, California, the company develops and markets hardware and software payment products that combine merchant services and mobile payments into a single, easy-to-use platform.
Out of their many robust products and services, people are most likely to be familiar with the Square iPad point-of-sale system used in many businesses across the country. Square generates billions of monthly transactional data, which forms the basis of analysis at the company. Data science is at the very core of Square’s products and features, and its data scientists are entrenched within various teams in a dynamic capacity to interpret and discern the vast amount of data that Square generates daily.
The data science role at Square vary greatly depending on each team’s unique goals and priorities and cuts across a wide range of data science and analytics concepts. Depending on the team and product features, the role may range from mildly technical, business/financial analytics-focused to deploying more technically advanced machine learning/deep learning algorithms. Thus, the tools and skills required may also range from basic analytics to writing code to deploying machine learning systems.
Square hires qualified individuals with 2+ years of relevant industry experience. Other relevant qualifications include:
The term data science at Square encompasses a wide scope of fields related to data science. Data science roles at Square will likely be categorized under the title of data scientist, data analyst, machine learning specialist, or product analyst. The roles and functions may range from product-focused business analytics to more technical machine learning/deep learning tasks.
More specifically, the data science roles at Square may include one or more of the following team-specific responsibilities:
“We strive to make our interview process a true reflection of our culture: transparent, mindful, and collaborative. Throughout the interview process, your recruiter will partner closely with you and guide you through the next steps.
At Square, we want all our candidates to feel they can thrive during the interview process. We know that finding and choosing a job is deeply personal, and our team is here to help you guide through that journey.”
-Taylor Cascino, Head of Talent
In the Square Data Scientist interview process, the most commonly tested skills are in Python, SQL and Algorithms. This is compared to regular Data Scientist interviews that typically ask SQL and Machine Learning.
Square has four stages of the interview process, where they ask candidates various data science interview questions.
1. Pre-Onsite Interviews
After reviewing feedback with the hiring teams, we’ll recommend moving forward to an onsite or to not move forward at this time.
2. Onsite Interview
L6+ Candidates may replace one of the Interviews with a Q&A Leadership interview.
3. Hiring Bar Review
4. Offer Decision
Data science interview questions such as SQL will screen your understanding of SQL principles that are not specific to any language. Generally speaking, Data Scientists at Square are familiar with Snowflake SQL.
During the interview, you’ll write and compile code in a collaborative online IDE (CodeSignal). If you are new to using this tool, please review these preparation materials to ensure you are ready to go!
The interviewer will present a question, and you’ll have approximately 30 minutes to come to a working solution.
This pair programming screen tests your understanding of programming principles as well as your ability to solve a problem with Python or R using built-in data structures (e.g., dictionary) and functions (e.g., set ()) in the language.
Similar to the SQL interview, you will also be using CodeSignal for this interview.
This is a conversational interview between you and your potential Manager. You are getting to learn more about the team, project, and scope of the role while the Hiring Manager is gaging your particular fit and interest in their specific team.
There may be more than one HM screen conducted depending on availability and fit.
Below are topics that may be covered in the call:
Note: Topics may vary per team
1. Data Exploration & Engineering (60 minutes)
The exploratory analysis and dataset creation interview test your ability to explore and analyze a product dataset to answer business questions and design an aggregated table to enable future analysis and ML use cases. For the exploratory portion of this interview, you are allowed to use Python, R, Excel, Tableau, or Google Sheets, and you will need to share your screen using the tool(s) of your choice on your own computer.
2.Statistics & ML (60 minutes)
The modeling interview tests your ability to apply ML to solve a business problem: from data processing to analysis to knowledge of ML algorithms and how to evaluate performance. For this interview, you are allowed to use your coding language of choice (we recommend Python or R), and you may need to share your screen using the tool(s) of your choice on your own computer.
3. Operational Thinking (30 minutes)
This experimentation interview tests your familiarity with A/B testing and related statistical concepts, as well as your ability to inform product decisions in line with the test results. You will not need any tools for this interview, and we recommend coming prepared with a pencil and paper.
1. Influence & Collaboration (30 minutes)
The goal of this interview is to understand how you partner with various functions within a product team. You are expected to discuss how you have led projects and collaborated with cross-functional teams. Please be ready to discuss examples in detail.
2. Partnership Interview - Strategic Thinking (30 minutes)
The goal of this interview is to understand how you approach prioritization and open-ended projects. We are interested in hearing how you break down ambiguous problems/objectives into concrete analytics deliverables and how you help the team prioritize. Please be ready to discuss examples in detail as needed.
3. Partnership Interview - Sell (30 minutes)
This call will give you an opportunity to chat with the Manager for the role. They will be giving more details about their team and focus. You’ll also have an opportunity to ask more specific questions that you’d like answers to.
Note: L6+ Candidates may have an additional Q&A Leadership Interview
Average Base Salary
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Read interview experiences and salary posts in preparation for your next interview.
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