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 wouldn’t 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, we’ve 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 – 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. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.
📍Build 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 📍 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 📍Produce thorough documentation of our program that can withstand regulatory scrutiny 📍Proactively identify new opportunities and future needs of our ML teams 📍Lead by example by applying ML and engineering best practices 📍Stay current on ML developments in the field, foster an environment of continuous learning, and apply new learnings when applicable 📍Have a significant impact on influencing team culture and direction
You Have: 📍5+ years of software engineering or machine learning experience 📍A degree (preferable graduate level) in Computer Science, Engineering, Statistics, Physics, Applied Math or a related technical field 📍Practical experience working with both deep learning and traditional machine learning frameworks 📍Prior experience working with product, engineering, and business to prioritize, scope, design, and deploy ML tooling and infrastructure at scale 📍Natural curiosity and desire to grow and help shape all aspects of our small and growing team 📍Excellent written and oral communication skills and are comfortable working with a cross-functional, globally distributed team