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

We are looking for experienced Data Scientists to join Square's growing Capital organization. We have an incredible team that is working to make financing easier, fair and more transparent for all businesses. As a Data Scientist on the Capital Team, you will leverage analytics, engineering, and machine learning to empower data-driven decision making in the full lifecycle of product development. Capital is expanding it's product set to help sellers meet their varying  financing needs. You would partner with the various teams to drive this initiative. You will work closely with product managers and machine learning engineers. You will leverage data to predict credit and fraud risk, improve strategic decisions, and lead experimentation/growth projects.

This role will be embedded within a product team.

You Will:

• Partner with multiple team members to make data-driven decisions across the organization by leveraging descriptive and predictive analytics to produce material impact
• Provide comprehensive analytics support to partner teams, primarily through development of self-serve tools (such as ETLs and Looker)
• Determine and monitor essential metrics for product projects
• Own, coordinate, and solve complex, cross-functional problems that extend beyond the traditional boundaries of product domains, analytics, and data science
• Communicate analysis and decisions to high-level stakeholders and executives in verbal, visual, and written media
• Apply a diverse set of techniques including statistics, quantitative reasoning to help grow the business

Qualifications

• 3+ years of analytics experience or equivalent
• Fluent in data tools for analysis and visualization (SQL, dashboards, Python/R)
• Strong problem solving skills and ability to translate ambiguous, unstructured problems into actionable data-driven analyses
• Skilled verbal and written communication to explain technical analysis to non-technical partners
• Familiarity with data warehouse design, development and best practices
• Proven ability to lead projects that depend on the contributions of others in multiple disciplines
• Experience in applying both data-backed heuristics and machine-learning techniques to solve practical product problems such as funnel optimization. Bonus points for experience applying these techniques to underwriting.
• Some background in lending, finance or risk is helpful but can be learned on the job

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