Data Scientist, Square for Retail

  • New York, NY
  • Full-time
  • Position open to remote: Yes
  • Current Square Employee?: Apply via go/jobs with your Square email.

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

Data Scientists at Square are embedded within product teams and leverage engineering, analytics, statistics, and machine learning to empower data-driven decision making in the full life cycle of product development. From the cozy local wine store to the trendy sneaker pop-up, the Retail team at Square is finding new ways to equip retail sellers and their staff with the tools they need to succeed. Square’s mission is economic empowerment, and Data Scientists support this by using data to understand and empathize with our customers, thereby enabling us to build a remarkable product experience. 

You will:

  • Partner with the product stakeholders to identify, prioritize, and answer the most important questions where analytics will have a material impact and produce actionable insights

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

  • Apply a diverse set of tactics such as statistics, quantitative reasoning, and machine learning; discerning where simple analytics solutions (e.g. a quick visualization) are preferable to complex solutions (e.g. machine learning).

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

  • Contribute to the data strategy of product engineering, influencing engineers to make well-informed architecture and design decisions that affect data at Square

Qualifications

You have:

  • 3+ years of data science experience or equivalent

  • Fluency in SQL

  • Experience performing data analysis and machine learning using Python or R

  • Experience using statistics to inform decision making (e.g. A/B testing)

  • Ability to understand complex business and data systems; versatility and willingness to learn new technologies on the job

Even better:

  • An advanced degree (B.S., M.S., PhD.) in Mathematics, Statistics, Computer Science, Physical Sciences, Economics, or a related technical field

  • Experience with a BI tool such as Looker or Tableau 

  • Experience with data warehouse design, development and best practices

Technologies we use and teach:

  • SQL (Snowflake)

  • Python (pandas, scikit-learn, etc.)

  • ETL 

  • Looker, Amplitude, Tableau

Additional Information

At Square, we value diversity and always treat all employees and job applicants based on merit, qualifications, competence, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us by sending an email to assistance(at)squareup.com. We will treat your request as confidentially as possible. In your email, please include your name and preferred method of contact, and we will respond as soon as possible.

Perks

At Square, 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