Lead Machine Learning Scientist, Lending

Varo is a completely new type of bank. It's a fully digital, mission-focused, FDIC-insured bank designed to fit the lifestyle of our customers. It's truly a bank for everyone. About the Opportunity Varo embodies a new breed of fintech, harnessing technology and innovation to empower consumers. We serve millions of Americans with our mobile app, making digital banking experience easy, convenient, and personalized. We believe the future of fintech lending is tied to Machine Learning. Be one of the first scientists in the world to innovate models that dynamically set credit limits, decide loan eligibility, and predict customer borrowing behavior. You'll lead Machine Learning in lending at Varo, working on predictive insights and underwriting models that enable wider, more inclusive, and affordable access to credit for our customers. The Lead Machine Learning Scientist in Lending will be responsible for developing the machine learning models underlying Varo’s lending products. You will manage the development and training of these models and also contribute to the design of architecture that serves the models. About the Data Science Team Varo’s Data Science Team develops machine learning models that streamline access to funds for the right people, protect customers from fraud and transform the in-app experience with real-time recommendations. At Varo, data science is front and center, unlike other organizations where it plays a more supportive role. Since we are in a hyper-growth mode, you will get to work on significant data science problems from day one. What you'll be doing - Lead organizational efforts to design, develop, optimize, and actualize ML-based lending and underwriting models - Acquire both traditional and alternative data sets and transform them into practical predictive features - Work with stakeholders in the lending organization to identify opportunities to drive business decisions or improve our product suite with ML - Collaborate cross-functionally with the engineering team to deploy models and monitor outcomes - Guide and mentor junior machine learning scientists and engineers You’ll bring the following skills and experiences - 5+ years expertise in developing ML industry applications for lending or risk applications - Demonstrated experience in building credit risk models and underwriting models - Ph.D. or equivalent in Economics, Computer Science, Statistics, or a related field - Strong team player and an active problem solver - Ability to thrive in a fast-paced environment - Preferred experience: Familiarity with Sagemaker, Spark, TensorFlow, Keras, and PyTorch We understand not everyone will meet all these requirements. If you meet most of the criteria and are excited about the opportunity and willing to learn, we’d love to hear from you! About Varo Launched in 2017, Varo aims to bring the best of fintech into the regulated banking system. We're a new kind of bank - all-digital, mission-driven, FDIC-insured, and designed around the modern American consumer. As the first consumer fintech to be granted a national bank charter in 2020, we aim to make financial inclusion and opportunity a reality for everyone. Our Core Values - Customers First - Take Ownership - Respect - Stay Curious - Make it Better Learn more about Varo by following us on our various social media handles and our blog. Varo is an equal opportunity employer. We embrace diversity and are committed to building teams that represent a variety of backgrounds, perspectives, and skills. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Beware of fraudulent job postings! Varo will never ask for payment to process documents, or refer you to a third party to process applications or visas, or ask you to pay costs. Never send money to anyone suggesting they can provide work with Varo. If you suspect you have received a phony offer, please e-mail [email protected] with the pertinent information and contact information.