Staff Data Scientist (ML, Credit Risk)

Description du poste

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards The Credit Card business team’s mission is to shape Robinhood’s vision in the credit and banking space by delivering smart, customer-focused financial solutions Our team is dedicated to reshaping the credit landscape and redefining the way people interact with financial services daily. We leverage cutting-edge analytical tools and diverse datasets to build deep understandings of consumer credit behaviors. We aim to make financial services accessible to everyone, building programs that support Robinhood’s broader goals! As a Staff Data Scientist, you will build credit risk models that allow us to better serve our customers and make responsible lending decisions Credit models are at the heart of all lending decisions. These models will drive decisions ranging from approve/decline, line assignment at origination and future credit limit increases. You will leverage traditional and non-traditional data sources to build highly predictive risk models. You will own the full lifecycle of model development from data prep, model building to model deployment Build credit risk models for customer acquisitions (credit approval and credit limit assignment) and customer management Collaborate with credit analysts and product managers to understand the business problem and deliver appropriate models to solve them Create rich datasets leveraging both traditional and non-traditional data sources. Work on innovative tools to generate powerful features that boost models Deploy, maintain and monitor models in production Communicate with senior s

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