Data Scientist, Square Staff - Payroll

Data Scientist, Square Staff - Payroll

This job is no longer open

Company Description

Square builds common business tools in unconventional ways so more people can start, run, and grow their businesses. When Square started, it was difficult and expensive (or just plain impossible) for some businesses to take credit cards. Square made credit card payments possible for all by turning a mobile phone into a credit card reader. Since then Square has been building an entire business toolkit of both hardware and software products including Square Capital, Square Terminal, Square Payroll, and more. We’re working to find new and better ways to help businesses succeed on their own terms—and we’re looking for people like you to help shape tomorrow at Square.

Job Description

Square’s mission is economic empowerment, and our team supports this by using data to understand and empathize with our customers, thereby allowing us to build a remarkable product experience. As a member of the Square Staff team, you will use engineering, analytics, and machine learning to empower data-driven decision-making in the full lifecycle of product development and bringing our products to market. You will partner with the product team to lead experimentation & growth plans, partner with engineering to implement comprehensive and reliable data logging, develop solutions to personalize product experiences, provide insights to our sellers about their business, and drive strategic decisions with data. We are looking for seasoned and passionate data scientists to join our data team for the each of following product areas:

Payroll - In addition to enabling businesses to take credit card payments, Square offers a variety of add-on features to help run and grow a business. Square Payroll is a full-service payroll, automated tax filing, and employee benefits system. As a data scientist on the team, you will help drive and execute on product and go-to-market initiatives for Square Payroll. You will collaborate with Product Managers, Product Marketing Managers, Engineers and Designers to optimize the product experience for driving acquisition and retention to deliver value to our broad community of Sellers.

You will:

  • Work directly with the product team to make data-driven decisions across the organization by applying descriptive and predictive analytics where it will have a material impact

  • Apply a diverse set of tactics such as statistics, quantitative reasoning, and machine learning to research and produce insights

  • Lead, coordinate, and execute on complex projects that extend beyond the traditional boundaries of product domains, analytics, and data science

  • Communicate analysis and decisions to high-level partners and executives in verbal, visual, and written media

  • Lead the data strategy of embedded product engineering, to help make well-informed architecture and design decisions that affect data at Square

  • Develop resources to empower data access and self-service analytics so your expertise can be leveraged where it is most impactful


You have:

  • 3+ years of analytics and data science experience or equivalent

  • B.S or Ph.D. in a quantitative field (mathematics, statistics, or similar STEM field)

  • Experience building relationships to influence product partners with data

  • Experience participating in cross-functional projects that depend on the contributions of others in multiple disciplines

  • Experience applying both statistical and machine-learning techniques to solve practical product problems such as predicting churn, LTV, cross-selling, and clustering user archetypes

  • Familiarity with data warehouse design and best practices

  • Fluency with data, analytics, and visualization technologies (we use SQL, Looker, and Python)

  • Fluency with general coding and scripting skills

Additional Information

We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, without regard to race, color, religion, gender, national origin, age, disability, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page

Additionally, we consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.


We want you to be well and thrive. Our global benefits package includes:

  • Healthcare coverage
  • Retirement Plans
  • Employee Stock Purchase Program
  • Wellness perks
  • Paid parental leave
  • Paid time off
  • Learning and Development resources

Block, Inc. (NYSE: SQ) is a global technology company with a focus on financial services. Made up of Square, Cash App, Spiral, TIDAL, and TBD, we build tools to help more people access the economy. Square helps sellers run and grow their businesses with its integrated ecosystem of commerce solutions, business software, and banking services. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin. Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects. Artists use TIDAL to help them succeed as entrepreneurs and connect more deeply with fans. TBD is building an open developer platform to make it easier to access Bitcoin and other blockchain technologies without having to go through an institution.

This job is no longer open
Logos/outerjoin logo full

Outer Join is the premier job board for remote jobs in data science, analytics, and engineering.