Square Data Scientist Interview Questions + Guide in 2025

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 nationwide.

Square generates billions of monthly transactional data, forming the basis of company analysis. 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.

This interview guide gives you a glimpse of the interview process and Square data scientist interview questions you must know if you are aspiring for this role.

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.

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.

“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

• 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 assess your understanding of core SQL concepts rather than language-specific syntax. Data Scientists at Square typically work with Snowflake-style SQL, and fluency with common analytical patterns is expected.

During the interview, you will write queries in a shared online editor, most commonly CoderPad. Candidates report that this round is fast-paced and highly time-constrained, with approximately 7 to 8 SQL queries to complete within a 30-minute window.

Rather than focusing on a single deep problem, this interview emphasizes speed, accuracy, and pattern recognition. Questions often involve grouping, filtering, and ranking data efficiently, including the use of window functions such as ROW_NUMBER, RANK, or DENSE_RANK. Interviewers expect candidates to move quickly from one query to the next while maintaining correct logic under pressure.

Python/R Interview

This pair programming screen evaluates your ability to solve data-focused problems using Python or R with built-in data structures such as lists, dictionaries, sets, and strings.

Similar to the SQL interview, this round is conducted in a shared online coding environment.

• You may choose either Python or R. Select the language you are most comfortable with rather than one you recently learned.
• You will have approximately 45 minutes to come to a working solution.
• This is not a software engineering interview and does not focus on advanced data structures or algorithms.
• You will be evaluated on basic scripting proficiency, clarity of logic, and correctness while manipulating data without external libraries.

Hiring Manager Screen (Team Fit)

This is a conversational interview with your potential manager, focused on understanding your background, interests, and alignment with the team’s work. You will also learn more about the team’s scope, priorities, and projects.

Depending on availability and team needs, there may be more than one hiring manager screen.

Topics that may be covered include:

• Managing high-volume data and maintaining data hygiene
• Data strategies and toolkits such as SQL or Python
• Examples of data-driven decisions that improved efficiency or effectiveness
• Problem-solving experience in cross-functional environments
• Projects where you influenced product or business decisions

Note: Topics may vary by team.

Overall Prep for Pre-Onsite Interviews

• Interviewers want to hear your thought process as you work. Speak clearly about your approach, especially when choosing between alternatives or making tradeoffs.
• Be mindful of strict time constraints, particularly in the SQL round. Prioritize producing correct, working solutions quickly rather than optimizing prematurely.
• If you encounter a roadblock, communicate your reasoning and ask clarifying questions when appropriate.
• Expect follow-up questions or extensions once you complete an initial solution, as many problems are designed to test depth and leveling.
• Plan to leave 5 to 10 minutes at the end for questions about the team, culture, or role.

Preparing for the Onsite Interviews

1. Data Exploration & Engineering (60 minutes)

This interview assesses your ability to explore a product dataset, answer business questions, and design aggregated tables for downstream analysis or machine learning use cases. You may use Python, R, Excel, Tableau, or Google Sheets and will share your screen using your own setup.

2. Statistics & ML (60 minutes)

This round evaluates how you apply statistical and machine learning techniques to real business problems, from data preparation to model selection and performance evaluation. You may code in your preferred language, typically Python or R, and should be prepared to explain your modeling decisions clearly.

3. Operational Thinking (30 minutes)

This interview focuses on experimentation, A/B testing, and statistical reasoning in product contexts. You will not need any tools and should be comfortable reasoning through results and implications verbally.

Partnership Interviews

1. Influence & Collaboration (30 minutes)

This interview explores how you work with cross-functional partners and lead projects within a product team. Be prepared to discuss specific examples in detail.

2. Partnership Interview – Strategic Thinking (30 minutes)

This round focuses on prioritization and ambiguous problem-solving. Interviewers want to understand how you break down open-ended objectives into actionable analytics deliverables and help teams make informed tradeoffs.

3. Partnership Interview – Sell (30 minutes)

This conversation with the manager provides deeper insight into the team’s mission and expectations. You will also have the opportunity to ask role-specific questions.

Note: L6+ candidates may have an additional Q&A Leadership interview.

• Square in the News: Learn about recently shipped products, partnerships, and releases from Square’s media center

• Inclusion & Diversity Blog: Learn more about our Inclusion & Diversity processes and programs that have driven progress.

• Engineering Culture Guide: Learn about the building blocks that make up Square’s engineering culture.