Square Data Scientist Interview Guide

Square Data Scientist Interview Guide


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

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.

Required Skills

Square hires qualified individuals with 2+ years of relevant industry experience. Other relevant qualifications include:

  • An advanced degree (M.S. or Ph.D.) in Computer Science, Software Engineering, AI, ML, Applied Mathematics, Statistics, Economics, Physics, or a related technical or quantitative field.
  • 2+ years (5+ for senior or 10+ for a lead role) of relevant industry experience in data science or machine learning-focused roles.
  • Experience deploying machine learning (e.g., regression, ensemble methods, neural networks, etc.) and Statistical (Bayesian methods, experimental design, causal inference) solutions to solving complex business problems.
  • Proficiency in the following scripting languages (Python, Java, etc.).
  • Experience with Hive, Google Cloud Platform, Looker, Snowflake, GCP, and AWS.
  • Experience building complex, scalable ETLs for various business and product use cases.
  • Technical expertise in building personalization, ranking, or recommendation systems that scale, with a fundamental understanding of machine learning algorithms and statistics.

Data Science Teams at Square

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:

  • Trusted Identity Team: Develop algorithms and cross-functional analytics to help understand Square’s customers and determine if users comply with Square’s terms and conditions under the law.
  • Capital: Develop analysis and build models that help drive originations and reduce losses for Square’s business loan products.
  • Bureau Organization: Develop real-time data infrastructure and build algorithms to personalize Square’s products and services marketing efforts while enabling robust decision-making across the organization.
  • Growth Data Science Team: Leverage data and automation to help Square solve impactful business, marketing, and growth problems such as lifetime value forecasting, churn prediction, attribution modeling, causal inference, and more.
  • Customer Support Automation (Cash App): Build models that anticipate customer issues and deliver proactive in-app suggestions, use NLP to contextualize inquiries and respond instantly with relevant content, develop prioritization algorithms that improve efficiency, and apply the latest research to automate conversations with customers.
  • Risk (Cash App): Build machine learning models that detect fraudulent activity in real-time, develop new product features that drive down risk losses, experiment with state-of-the-art algorithms to decrease false positives, and use any and every dataset at your disposal (including 3rd party data) to engineer new features for risk models, verify customer documents using OCR, and use biometric and device signals to detect malicious logins and account takeovers.
  • Compliance Team: Build and automate actionable reporting, define KPIs, build ETLs, and build dashboards for key compliance processes to improve the overall compliance infrastructure and platforms.
  • Embedded Product: Leverage engineering, analytics, and machine learning to empower data-driven decision-making in the full life cycle of product development while working cross-functionally across many different team organizations.

How Square Interviews

“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

Square Data Scientist Interview Process

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

  • SQL interview (30 minutes)
  • Python/R interview (45 minutes)
  • HM Screen(s) (30 minutes)

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

  • Data Engineering & Exploration (60 minutes)
  • Statistics & ML (60 minutes)
  • Operational Thinking (30 minutes)
  • Influence & Collaboration (30 minutes)
  • Strategic Thinking (30 minutes)

L6+ Candidates may replace one of the Interviews with a Q&A Leadership interview.

3. Hiring Bar Review

  • Interview feedback is reviewed by the Data Science leadership team
  • Final level decision is determined
  • Final approval for hire is determined

4. Offer Decision

  • With approval from Hiring Bar, you’ll work with your recruiter through the offer process.

Preparing for the Pre-Onsite Interviews

SQL Interview

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.

Python/R Interview

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.

  • You can program in either Python or R. We recommend choosing the language you are most proficient in, rather than one you just recently learned or feel is more conducive to the interview process.
  • The interviewer will present a question, and you’ll have approximately 45 minutes to come to a working solution.
  • This is NOT a Software Engineering interview. This is NOT about data structures or algorithms. It will be more about lists, dictionaries, and strings.
  • You will be manipulating data without a library and evaluated on basic proficiency and scripting.

Hiring Manager Screen (Team Fit)

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:

  • Managing high volume data and organizing data (data hygiene)
  • Data strategies and tool kits (technology like SQL or Python)
  • Example of a situation you made data-driven decisions that impacted the company (make more efficient/effective)
  • Problem-solving and cross-functional team experience
  • A project where you influenced product decisions

Note: Topics may vary per team

Overall prep for Pre-Onsite Interviews

  • We want to hear your thought process as you solve the problem. Please speak up, especially if you hit a roadblock. We encourage you to use whatever tools (google and other docs) you’d use in your normal working day. Collaboration is an important part of Square’s culture, and we want to collaborate with you throughout the interview.
  • Feel free to ask clarifying questions if you do have questions about “how”, you should be able to describe your alternatives to the interviewer and then discuss their merits.
  • Consider the time constraints when solving the problem. Speak up about tradeoffs you are making and things that you would write differently. Feel free to ask your interviewer for guidance on approaches, including balancing code maintainability vs. reaching a working solution quickly.
  • Show expertise in your preferred language. You should know commonly used libraries well or be able to look up needed documentation on the web. Even if you feel you do know them already, consider having reference docs for your preferred language handy in case you need it during the assessment.
  • Be mindful that if you solve the initial ask, the interviewer will likely ask you to iterate on your solution, as every question has multiple parts to it. This attributes to leveling.
  • There should be about 5-10 minutes in the end to ask questions about our teams, company culture, or anything else on your mind.

Preparing for the Onsite Interviews

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.

Partnership Interviews:

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

Square Data Scientist Interview Questions

Product Metrics
Machine Learning
ML System Design
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Square Data Scientist Salary


Average Base Salary


Average Total Compensation

Min: $101K
Max: $193K
Min: $110K
Max: $310K

View the full Data Scientist at Square salary guide

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