Senior Machine Learning Modeler, Cash App
- Austin, TX, USA
- Employees can work remotely
- Alternate Location: Austin, United States
Cash App is the fastest growing financial brand in the world. Initially 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 app with over 36 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.
Loved by customers and pop culture, we’ve consistently held the top spot for finance in the App Store for many years, seeing more engagement with millions of followers across social media in a day than most brands see in a year. We are building an ecosystem to redefine the world’s relationship with money by making it universally accessible.
We want to hire the best talent regardless of location. Our employment model is distributed, offering the opportunity to collaborate with teams across the world in San Francisco, New York, St. Louis, Portland, Toronto, Kitchener-Waterloo, Sydney, and Melbourne.
Machine Learning is an integral part of how we design products, operate, and pursue Cash App’s mission to serve the unbanked as well as disrupt traditional financial institutions. Our massive scale and deep trove of transaction data create an endless number of opportunities to use artificial intelligence 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.
This role is part of our Cash App's ML team and will be deeply embedded within one of our product teams - here are the workstreams we're currently hiring for:
You'll build machine learning models that detect fraudulent activity in real time and help keep our customers safe and secure. 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.
Our Lending team works on models that drive marketing, pricing, and risk management for our consumer lending products. Through your work you will empower the team to understand the financial performance of origination cohorts, optimize our automated decisioning pipeline using AI/ML, and help identify new opportunities for growth in our customer base. You will experiment with various modeling techniques on our comprehensive customer data and see your solution through to production by partnering cross-functionally with finance, product, and engineering teams. This role is best suited to someone with prior experience in the consumer credit or lending space.
Working closely with the Banking Product and Financial Platform teams, you will lead development of analytical and ML solutions that utilize our data to power optimizations across our Cash debit card and bank account products. Your work will aim to increase the customer adoption of banking products, reduce the manual operational load caused by banking features, and grow the overall revenue of Cash Banking as a whole. You'll be required to identify ML opportunities, prioritize your time, and solve complex problems where ML will have a significant impact for both our banking customers and/or the operation teams that support them.
As part of Cash App’s Network product workstream, you will design and build AI/ML systems to help millions of Cash App customers pay and get paid with new and exciting experiences. Through your work, you will empower the workstream to better understand our customers’ payment journeys, personalize their payment experiences using AI/ML, and help identify new opportunities for product improvement. You will employ a variety of modeling techniques on our comprehensive customer data and scale your solution to production by partnering closely with design, product, analytics, and engineering teams. We are open to have remote members join for this position.
Recommendations & Incentives
Cash App has a multitude of products for customers to use and make sense of including Boost Rewards, Fractional Share Investing, Bitcoin, and Peer-to-Peer Payments. On this team, you will work closely with Product and Engineering to drive customer satisfaction, engagement, and acquisition via personalized recommendations and optimized incentives around these Cash App product areas. You will build models that surface relevant products, features, and rewards while reducing decision fatigue and overload. You will get to be a champion for the customer by selecting metrics that balance customer needs with business requirements. You will have opportunities to use a variety of approaches including recommender systems, collaborative filtering, and deep learning.
Technologies we use (and teach):
- Python (NumPy, Pandas, sklearn, xgboost, TensorFlow, keras, etc.)
- MySQL, Snowflake, GCP/AWS and Tableau
- 3+ years experience with applied Machine Learning or Deep Learning
- A graduate degree in Computer Science, AI, ML, Applied Math, Stats, Physics, or a related technical field
- Worked 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
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 local guidelines.