Square Data Scientist, Operations in San Francisco, California
As a Data Scientist at Square, you will lead projects that derive value from our unique, rich, and rapidly growing data. We’re a passionate team of hackers, statisticians, and optimizers who are resourceful in distilling questions, wrangling data, and driving decisions.
As a Data Scientist at Square, you will work with one or both of these teams:
The Compliance team at Square is responsible for policing the behavior of merchants on our platform to guarantee compliance with the Bank Secrecy Act and Anti-Money Laundering laws, ensure the security of our money transmission licenses, and fulfill financial institution partnership obligations, which are the cornerstone of Square’s ability to do business.
You will lead the development of fraud detection algorithms and systems that protect Square and its customers from fraud and financial loss. You will partner with Square’s Compliance team to identify, prioritize, and solve complex problems where analytics and data science will have a significant impact.
The Customer Success team at Square is responsible for making sure a customer’s support experience is as effortless as possible by developing scalable tools and services that help resolve any support issue efficiently while still maintaining a customer’s trust in Square.
As a Data Scientist working on Customer Success you will lead the development of algorithms that predict a customer’s support issue and use that information to help inform our operations team and self-service tools. You will work closely with Square’s Customer Success team, product teams and engineers to identify and prioritize machine learning projects that will have significant impact on our customers’ experience
*You will: *
Drive cross functional analytics projects from beginning to end: build relationships with partner teams, frame and structure questions, collect and analyze data, as well as summarize and present key insights in support of decision making
Work with engineers to evangelize data best practices and implement analytics solutions
Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models
Use your experience in analytics tools and scientific rigor to produce actionable insights
Uses machine learning to optimize our ability to address Compliance and Support concerns
Communicate key results to senior management in verbal, visual, and written media
Develop risk models that enable financial services for our customers
Help build the next generation of data products at Square
An advanced degree (M.S., PhD.), preferably in Statistics, Computer Science, Physical Sciences, Economics, or a related technical field
A consistent track record of performing data analysis using Python (numpy, pandas, scikit-learn, etc.) and SQL
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
2+ years industry experience in data-science or analytics
Familiarity with other data tools such as Hive, Vertica, Tableau, Ruby
Familiarity with Linux/OS X command line, version control software (git), and general software development
Technologies we use and teach:
Python (numpy, pandas, sklearn) & R
MySQL, Vertica, Hive, Redshift
Machine Learning (e.g. regression, ensemble methods, etc.)
Statistics (Bayesian methods, experimental design, causal inference)
Google Cloud Platform
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.