ML Engineering Manager (Support), Cash App
- San Francisco, CA, USA
- Current Square Employee?: Apply via go/jobs with your Square email.
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 24 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 an experienced leader to join our Support ML team and help build Cash App, the fastest growing financial app in the world. Our mission is to make banking and financial services accessible to the underserved and unbanked. The Support ML team uses our large and unique datasets to model, understand, and predict issues that customers may be experiencing with our services. The team relies heavily on NLP techniques to extract information from text data to deliver automated real-time communication and support to our customers.
You will be a part of the broader Data Science team and will report directly to the head of the organization. You will partner very closely with our product teams to ideate projects and deliver value to our customers through the use of machine learning in production. You will also work very closely with the members of your team to identify new and large opportunities that will shape the Support ML roadmap. We are looking for someone who will thrive in a fast-paced environment and will be hands on when needed.
- Recruit, lead, and mentor a strong team (current team size of 4-5, with future growth planned)
- Dive in deeply with members of your team to provide coaching and ensure their success, providing hands-on technical guidance and direction in critical high-impact areas as needed
- Partner and collaborate closely with Cash App’s product and business leaders
- Work cross-functionally with product, platform, and data engineering teams to prioritize efforts that will bring your team’s work to life
- Identify new opportunities, develop prototypes, achieve buy-in from partners, and communicate staffing needs
- Effectively communicate your team’s work with senior leadership and the executive team on a regular basis
- Stay up-to-date with the latest state-of-the-art techniques in machine learning and foster a culture of learning within the organization
- Help shape Cash App’s ML and Customer Support strategies
- 5+ years of machine learning and data science experience in industry with some people management experience
- An advanced degree in Machine Learning, Computer Science, Math, Statistics, or a related technical field
- Expertise in applying machine learning to solve large, complex business problems in a production setting
- Deep knowledge of and experience with a range of ML algorithms and techniques (especially NLP)
- Strong software engineering fundamentals and the ability to write production code when needed
- Strong statistical and mathematical intuition with an appreciation for concepts such as causality, selection bias, incrementality, hypothesis testing, etc.
- Experience in a fast-paced high-growth tech environment
- Intellectual curiosity and a passion for Cash App’s mission of economic empowerment
Technologies we use and teach
- Python (and its data analysis and machine learning libraries / frameworks)
- SQL (Snowflake), Airflow, Tableau
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.