Data Scientist, Growth Data Science

  • San Francisco, CA, USA
  • Full-time
  • Current Square Employee?: Apply via go/jobs

Company Description

We believe the economy is better when everyone has access. When everyone has room to grow. No one should be left out because the cost is too great or the technology too complex. We started with a little white credit card reader but haven’t stopped there. We’re empowering the independent electrician to send invoices, setting up the favorite food truck with a delivery option, helping the ice cream shop pay its employees, and giving the burgeoning coffee chain capital for a second, third, and fourth location. We’re here to help sellers of all sizes start, run, and grow their business—and helping them grow their business is good business for everyone.

Job Description

As a Data Scientist within the Growth Data Science team, you will 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. Our team provides Square with quantification and understanding of intent, behavior, value, and the potentially complicated ways in which they all interrelate. We currently own a variety of products that serve those purposes, including predictions, models, frameworks, and infrastructure. Our team looks across Square to obtain a holistic view of our business; the decisions we help facilitate tend to optimize globally across the company.

Qualifications

You will:

  • Drive cross functional data science projects from beginning to end: build relationships with partner teams, frame and structure questions, collect and analyze data, research, prototype, build out data science pipelines and models in production, and present methodology and key insights to stakeholders

  • Use a diverse set of techniques spanning machine learning, causal inference, and other forms of statistical modeling to solve important business and product problems.

  • Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models

  • Help build the next generation of data products at Square

You have:

  • 2+ years industry experience in data science or machine learning-focused roles

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

  • A strong track record of performing data analysis using Python (numpy, pandas, scikit-learn, etc.) and SQL

  • Familiarity with Linux/OS X command line, version control software (git), and general software development

  • Experience using statistics and machine learning to solve complex business problems

  • The versatility and willingness to learn new technologies on the job

  • The ability to clearly communicate complex results to technical and non-technical audiences

Additional Information

All your information will be kept confidential according to EEO guidelines.

At Square, our purpose is to empower – within and outside of our walls. In order to build the best tools for the businesses and customers we support all over the world, we have to start at home with a workforce as diverse and empowered as our sellers. To this end, we take great care to evaluate all employees and job applicants equally, based on merit, competence, and qualifications. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage candidates from all backgrounds to apply and always consider qualified applicants with arrest and conviction records, in accordance with 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.