Machine Learning Modeler, Cash App
- San Francisco, CA, USA
- Current Square Employee?: Apply via go/jobs
Cash App is the fastest growing financial brand in the world. Built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic money app with over 15 million active monthly users.
Loved by customers and by pop culture, we’ve held the #1 spot in finance on the App Store for almost two years, and our social media posts see more engagement in a day than most financial brands see in a year.
With major offices in San Francisco, New York, St. Louis, Portland, Kitchener-Waterloo, and Melbourne, Cash App is bringing a better way to send, spend, and save to anyone who has ever sought an alternative to today’s banking system.
We are looking for Machine Learning Engineers to help build Cash App, the fastest growing financial app in the world. Machine Learning is an integral part of how we design products, operate, and pursue Cash App’s mission to serve the unbanked and completely disrupt traditional financial institutions. Our massive scale and rich transaction data create an endless number of opportunities to use AI to better understand our customers and offer new products and experiences that can improve their lives. We are a highly creative group that prefers to solve problems from first principles. We move quickly, make incremental changes, and deploy to production every day.
MLE Modeler (NLP), Customer Support Automation
Working closely with our Customer Support team, you will use our vast amounts of data to make experiences seamless for our customers and help us achieve world-class service as Cash App continues its rapid growth. You will build models that anticipate customer issues and deliver proactive in-app suggestions, use NLP to contextualize inquiries and respond instantly with relevant content, develop prioritization algorithms that improve efficiency, and apply the latest research to automate conversations with customers.
MLE Modeler, Risk
Working closely with our Risk team, you will build machine learning models that detect fraudulent activity in real time. You will experiment with state-of-the-art algorithms to drive down false positives, collaborate on new product features to drive fraud losses down, use any and every dataset at your disposal (including 3rd party data) to engineer new features for risk models, verify customer documents using OCR, and use biometric and device signals to detect malicious logins and account takeovers.
MLE Modeler, Growth
Working closely with our Growth team, you will build machine learning models to optimize and predict customer acquisition, on-boarding, engagement, churn, and lifetime value (LTV). You will use our unique and vast data to build models that personalize the in-app experiences and make recommendations. You will use network analysis to understand the dynamics and virality of Cash App’s P2P network to inform marketing campaigns and customer referral programs.
Technologies we use (and teach):
- Python (numpy, pandas, sklearn, xgboost, TensorFlow, keras, etc.)
- MySQL, Snowflake, GCP, AWS, Tableau
- 3-5 years of relevant industry experience.
- A graduate degree in computer science, AI, ML, applied math, stats, physics, or a related technical field.
- Experience working with product, design, and engineering to prioritize, scope, design, and deploy ML models.
- A track record of providing mentorship and technical leadership.
- An appreciation for the connection between the software you build and the experience it delivers to customers.
- A strong desire to perform and grow in your role.
Cash App treats all employees and job applicants equally. Every decision is based on merit, qualifications, and talent. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with each office’s corresponding state and city guidelines.