Data Scientist, Capital
- San Francisco, CA
We believe everyone should be able to participate and thrive in the economy. So we’re building tools that make commerce easier and more accessible to all. We started with a little white credit card reader but haven’t stopped there. Our new reader helps our sellers accept chip cards and NFC payments, and our Cash app lets people pay each other back instantly. We’re empowering the independent electrician to send invoices, setting up the favorite food truck with a delivery option, and helping the ice cream shop pay its employees. And, with Square Capital, we have extended over $1 billion in funding to our sellers, helping them manage their cashflow and making it easy to invest in and grow their business. Let’s shorten the distance between having an idea and making a living from it. And make it easier for customers to shop and pay at their favorite businesses. 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.
As a Data Scientist at Square working on Capital, you will lead projects that derive value from our unique, rich, and rapidly growing data. Specifically, you will do analysis and build models which will help drive originations and reduce losses for our business loan products.
Specific problems you will solve include:
How can we detect fraud and avoid making loans to businesses who are unlikely to repay?
How should we size loans to balance risk and growth? What, if any, external data should we invest in to improve the performance of our credit models?
How do we optimize our marketing? For example, what is the optimal frequency and timing of emails?
2-4 years of relevant industry experience
Experience developing and deploying machine learning and statistical models
Strong quantitative intuition and data visualization skills for ad-hoc and exploratory analysis
The versatility to communicate clearly with both technical and non-technical audiences
A willingness to solve problems using whichever tool is most appropriate for the situation, balancing multiple business and technical constraints
A graduate degree in statistics, applied mathematics, computer science, physical sciences, or a similar technical field
Experience with lending and/or financial data
Technologies we use and teach:
Python (numpy, pandas, sklearn, xgboost, TensorFlow)
Google Cloud Platform