Cash App's mission is to serve the unbanked and completely disrupt traditional financial institutions and is currently the fastest growing financial brand in the world. Cash App has gone from a simple peer to peer payments product to a dynamic app with 40+ million monthly active users. We are bringing a better way to send, spend, invest, and save to anyone who has ever sought an alternative to the traditional banking system. Job Description
The Financial Crimes Technology team at Square finds and reports financial crimes activity across Cash App and Seller tools. We work globally with partners in business, engineering, counsel and product to guarantee we are providing a safe user experience for our customers while minimizing or eliminating bad activity on our platform.
We are leveraging Machine Learning as an integral part of our toolkit to fulfil our mission. At Square scale, we are monitoring hundreds of billions of dollars in transactions across traditional payment and blockchain networks. We uncover and put an end to money laundering, fraud, and illegal activities before they impact our users. Additionally, we improve workflow and case tools, adding features that empower agent productivity and automate the high volume of monitoring.
- Build classification and anomaly detection models to detect criminal & suspicious activity across Cash App's p2p, banking, debit card, equities and crypto products
- Develop customer risk rating models that facilitate efficient KYC reviews at onboarding and ongoing basis for CashApp accounts.
- Leverage innovative features and state of the art algorithms like Language Models & Graph Neural Networks to drive down false positives & improve agent productivity across Transaction Monitoring, and Sanctions queues
- Join a new, small, and growing team and have a significant impact on influencing team culture and direction
- Use Python (numpy, pandas, sklearn, xgboost, Pytorch, TensorFlow, keras, plotnine etc.), MySQL, Snowflake, GCP, AWS for developing models
- 4+ years of Machine Learning and Deep Learning experience
- A graduate degree in computer science, data science, operations research, applied math, stats, physics, or a related technical field
- Experience with advanced techniques like word embeddings, sequence modeling, and graph convolutional networks is a big plus
- Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
- Be able to communicate and influence business stakeholders
- Have a curious, passionate, growth-oriented mindset
We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Square is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, without regard to race, color, religion, gender, national origin, age, disability, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page .
Additionally, we consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.
Square, Inc. (NYSE: SQ) builds tools to empower businesses and individuals to participate in the economy. Sellers use Square to reach buyers online and in person, manage their business, and access financing. Individuals use Cash App to spend, send, store, and invest money. And TIDAL is a global music and entertainment platform that expands Square's purpose of economic empowerment to artists. Square, Inc. has offices in the United States, Canada, Japan, Australia, Ireland, Spain, Norway, and the UK.