Data Scientist, Trusted Identity 918 views

They believe everyone should be able to participate and thrive in the economy. So they’re building tools that make commerce easier and more accessible to all. They started with a little white credit card reader but haven’t stopped there. Their new reader helps their sellers accept chip cards and NFC payments, and their Cash app lets people pay each other back instantly. They’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. Let’s shorten the distance between having an idea and making a living from it. They’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 Trusted Identity team at Square, you will lead projects that derive value from our unique, rich and rapidly growing data. Specifically, you will lead the development of algorithms to help them understand who their sellers are and whether they are using Square’s services within the law.

Square’s mission is to make commerce more accessible to all. Trusted Identity’s mission is to build a data-driven approach to quantifying trust in their sellers. This approach gives Square the ability to accept as broad a group of sellers as possible onto their platform, including sellers that other payments processors may not accept, such as the underbanked, or sellers running non-traditional businesses. Trusted Identity sits at the top of the seller funnel, meaning that their work’s impact percolates through every element of the Square ecosystem. They use their concept of quantified trust to support offering Square to as many sellers as possible while also keeping Square safe from financial crimes, such as money laundering. You will lead the development of data solutions that ensure that Trusted Identity can continue to fulfill its mission efficiently and effectively as Square continues to scale.

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
• Partner with Square’s Compliance team to identify, prioritize, and solve complex problems where analytics and data science will have a significant impact – with a focus on aiding in Square’s compliance with the US Bank Secrecy Act and Anti-Money Laundering laws
• 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
• Use machine learning to optimize our ability to address Trusted Identity, Compliance, and general operational concerns
• Communicate key results to senior management in verbal, visual, and written media
• Help build the next generation of data products at Square

Qualifications

You have:

• 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
• Experience developing and deploying machine learning / deep learning solutions in production
• The versatility and willingness to learn new technologies on the job
• The ability to clearly communicate complex results to technical and non-technical audiences

Even Better

• 1-2+ years of industry experience in data science or machine learning-focused roles

Technologies We Use And Teach

• Python (NumPy, pandas, sklearn) & R
• SQL
• Machine Learning (e.g. regression, ensemble methods, neural networks, etc.)
• Statistics (Bayesian methods, experimental design, causal inference)
• Looker
• Google Cloud Platform