Square Data Scientist
Interview Guide May 03
May 033 rounds
The role of a Square Data Scientist
The role of a data scientist at Square requires a unique blend of technical and analytical skills. Your main responsibility will be to analyze large datasets and develop predictive models that can help Square optimize its products, services, and business strategies. You will also be responsible for designing and conducting experiments, evaluating the results, and communicating insights to stakeholders across the company.
Square's data science positions work across various domains, each with its unique set of challenges and opportunities. For instance, Square is primarily a payment processing and financial services company, so many data scientists work on improving payment processing, fraud detection, risk management, and other financial services. For example, they were recently looking for a Data Scientist to join their Risk Machine Learning & Decision Science team. In Product Development, data scientists work on developing and optimizing Square's various products and services, including Square Register, Square Terminal, and Square Online. There are many other domains where data scientists play important roles. For more updated information, we’d recommend checking Square’s careers page.
Square hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior and Staff level roles and some openings for Data Science Managers.
How to Apply for a Data Scientist Job at Square?
To apply for a Data Scientist job at Square, you will need to visit the company's career website and search for open Data Scientist positions. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
As a part of the Square Data Scientist interview, the candidate will need to go through multiple interview rounds:
1. HR Screening - The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company.
2. Technical Screening - The second round of the interview process is typically a technical screening, which will be conducted in two parts - SQL and Python coding.
3. Onsite Rounds - The final round of interviews will usually be a combination of technical rounds and behavioral rounds. You will have a panel of interviewers (often senior data scientists and business partners) who will assess you across a range of skills and experiences.
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Square Data Scientist: HR Screening
The first step in the interview process is typically an initial screening call with a Square HR representative. During this call, you can expect to discuss your background and experience in more detail, as well as answer questions about your interest in Square and the data science position.
- Why do you want to join Square?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Square?
- Describe a previous project of your choice, frame and solve a problem given a scenario.
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Square Data Scientist: Technical Screening
The second round of the interview process typically begins with a technical screening, which will be conducted in two parts.
The technical screen involves live coding exercises in both SQL and Python. The interviewer may ask you to write code to solve specific data science problems or to manipulate and analyze data in a particular way. You will be evaluated on your ability to write clean, efficient code and to solve problems using appropriate data science techniques.
SQL Interview: This part of the technical screening will be a more in-depth SQL interview. You may be asked to demonstrate your knowledge of SQL syntax and best practices, as well as your ability to use SQL to group, aggregate, windows function and join data. The interviewer may also ask you to explain your thought process and decision-making in working with a particular dataset or problem.
Python Interview: This part of the technical screening will be a more in-depth Python interview. You may be asked to demonstrate your proficiency in Python coding and to solve data science problems using Python libraries and tools. You may also be asked to explain your approach to working with different types of data and to discuss your experience with various Python libraries and packages. One candidate reported being asked basic questions related to strings in Python. Another candidate reported being asked questions related to loops in Python.
- What is a join? Can you explain the different types of joins in SQL?
- How would you approach finding the average revenue per customer for a given time period?
- What is a window function? Can you give an example of how you would use a window function in a query?
- How would you go about optimizing a slow-running SQL query?
- Can you explain the difference between a list and a tuple in Python?
- How would you approach finding the most frequent word in a text file using Python?
- What are some common data structures in Python? How would you choose which one to use for a given problem?
- Have you worked with any Python libraries for data manipulation or visualization? Can you describe your experience using them?
- Given 2 strings. Determine whether they can match in 0 or 1 swaps. StringA = "hello" StringB="oellh"
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Square Data Scientist: Onsite Rounds
In these onsite rounds, you will face multiple different rounds. You can expect to face one or more of the following:
- Case Study Round: In this round, you will be presented with a case study that is relevant to the work you would be doing at Square. You will be given a set of data and asked to analyze it and present your findings to a panel of interviewers. You may also be asked to come up with recommendations based on your analysis. The goal of this round is to assess your ability to think critically, analyze data, and communicate your findings effectively.
- Product Metrics Round: In this round, you will be asked to discuss key product metrics that you would use to measure the success of a Square product or feature. You will be evaluated on your understanding of key metrics in the industry, your ability to prioritize metrics based on business objectives, and your ability to communicate your reasoning clearly.
- Technical Rounds: In these rounds, you will be given a set of data and asked to process it and build a model to solve a specific problem. You may be asked to use a specific tool or programming language, and you will be evaluated on your ability to use appropriate data science techniques to solve practical problems. You may also be asked to explain your thought process and decision-making in working with the data and building the model.
- Behavioral: In this round, you will meet with a member of the team or a senior data scientist to discuss your fit with the company culture and values. You may also be asked about your experience working with cross-functional teams and how you approach collaborating with business partners.
- Can you walk us through your approach to analyzing the data provided?
- How did you decide which variables to focus on in your analysis?
- Based on your findings, what recommendations would you make to the business?
- How would you validate the results of your analysis?
- What are some key product metrics that you would use to measure the success of a Square product or feature?
- How do you prioritize metrics based on business objectives?
- Can you give an example of a time when you had to balance competing metrics in your analysis?
- How do you communicate complex metrics to non-technical stakeholders?
- Can you walk us through your approach to processing and cleaning the data provided?
- How did you choose which machine learning model to use for the problem at hand?
- How would you evaluate the performance of the model you built?
- Can you explain a time when you had to work with messy or incomplete data?
- How do you approach collaborating with business partners?
- Can you describe a time when you had to work with a difficult stakeholder?
- How do you handle ambiguity or uncertainty in your work?
- How do you stay up-to-date with the latest trends and techniques in the data science field?
Tips to stand out in the Square DS Interview
When you are preparing for a Square Data Science interview - we’d recommend the following things to keep in mind:
- Research Square and have a good understanding of their work culture. Check out their company culture page for more information about the work culture.
- Proficiency with both SQL and Python is important. Practice coding exercises to ensure that you are ready for the live coding exercises.
- Explain your thought process as you work through the problems. This will help the interviewer understand your approach and decision-making.
- If you are unsure about what the interviewer is asking, don't hesitate to ask for clarification. It's better to ask questions than to make assumptions and provide incorrect answers.
- Understanding the problem and the data provided will help you to come up with a plan before presenting your findings to the panel of interviewers.
- When presenting your findings or answering questions during the behavioral round, be clear and concise. Use concrete examples to illustrate your points.
Responsibilities of a Data Scientist at Square
The responsibilities of a data scientist at Square across roles can broadly be seen as-
- Diagnose problems and develop compelling, data-driven recommendations.
- Develop and maintain multiple data pipelines and ETLs.
- Apply a diverse set of techniques including statistical analysis, machine learning (ML), analytics, and data engineering to generate strategic insights.
- Collaborate with ML engineers on interesting ML initiatives ranging from categorization to search/recommendations.
- Partner with Product, Engineering, operation and other teams to design solutions to business problems, influence product roadmaps, and solution new products/processes.
- Promote creative risk solutions through third-party evaluation and integration with a focus on improving the seller experience.
- Communicate analysis and decisions to high-level partners and executives in verbal, visual, and written media.
Skills and Qualifications needed for Data Scientists at Square
Here are some skills and qualifications that will help you excel in your Data Science interviews at Square.
- It's beneficial to have at least 4+ years of experience in Data Science roles, which can help you stand out from other candidates. From what we’ve seen - candidates with less than 4 years of experience often struggle - even to get interview calls; let alone the interview loop.
- Proficiency in Python and SQL, as well as experience working with Looker or similar data visualization tools. (Square uses SQL, Python and Looker)
- A strong foundation in statistics, including experience with A/B testing and other common statistical techniques.
- Familiarity with standard machine learning concepts, such as regression, classification, and clustering, as well as experience applying these techniques to practical product problems.
- Understanding of data engineering best practices, including familiarity with data warehouse design and implementation.
- Experience leading cross-functional projects and collaborating with Product, Engineering, Marketing, and Design teams on strategy and prioritization.
- Experience applying both statistical and machine learning techniques to solve practical product problems such as predicting churn, LTV, cross-selling, and clustering user archetypes.
The salary range for a Square data scientist can be quite competitive. Entry-level data scientists at Square can expect a salary in the range of $100,000 to $140,000 per year. Mid-level data scientists with a few years of experience can expect a salary in the range of $130,000 to $180,000 per year. Senior data scientists or those with significant experience or leadership roles can expect salaries well over $200,000 per year.
The interview process for a Data Scientist role at Square typically includes 3 primary rounds - a HR screening, technical screening, and the onsite rounds. The first round is to have a quick discussion about your work experiences and the roles you’ve had in the past company. The second round of the interview process is typically a technical screening, which will be conducted in two parts - SQL and Python coding. The final round of interviews will usually be a combination of technical rounds and behavioral rounds. You will have a panel of interviewers (often senior data scientists and business partners) who will assess you across a range of skills and experiences.