About the company
Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldnāt work together. To solve this problem, we expanded into software and built integrated solutions to help sellers sell online, manage inventory, book appointments, engage loyal buyers, and hire and pay staff. Across it all, weāve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale. Today, we are a partner to sellers of all sizes ā large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. Weāre building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.
Job Summary
You will:
šBuild both batch and real-time machine learning solutions for the assessment of financial crimes risk at scale across all global markets in which Square operates š closely with stakeholders, business partners, and product engineering teams to ensure that data can be leveraged effectively to build efficient solutions within our regulatory program šProduce thorough documentation of our program that can withstand regulatory scrutiny šProactively identify new opportunities and future needs of our ML teams šLead by example by applying ML and engineering best practices šStay current on ML developments in the field, foster an environment of continuous learning, and apply new learnings when applicable šHave a significant impact on influencing team culture and direction
Qualifications
You Have: š5+ years of software engineering or machine learning experience šA degree (preferable graduate level) in Computer Science, Engineering, Statistics, Physics, Applied Math or a related technical field šPractical experience working with both deep learning and traditional machine learning frameworks šPrior experience working with product, engineering, and business to prioritize, scope, design, and deploy ML tooling and infrastructure at scale šNatural curiosity and desire to grow and help shape all aspects of our small and growing team šExcellent written and oral communication skills and are comfortable working with a cross-functional, globally distributed team
