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Staff Data Scientist, ML (Credit Risk)

Robinhood · Menlo Park, CA; New York, NY; Washington, DC

Postularme en la empresa
<div class=&quot;content-intro&quot;><h2>Join us in building the future of finance.</h2> <p>Our mission is to democratize finance for all. <a href=&quot;https://www.cerulli.com/press-releases/cerulli-anticipates-124-trillion-in-wealth-will-transfer-through-2048&quot; target=&quot;_blank&quot;>An estimated $124 trillion of assets</a> will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.</p></div><div class=&quot;markdown-document&quot;> <h2><span data-markdown-start-index=&quot;3&quot;>About the team + role</span></h2> <p><span data-markdown-start-index=&quot;25&quot;>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. </span><span data-markdown-start-index=&quot;451&quot;>Expectations are high, and so are the rewards. </span></p> <p><span data-markdown-start-index=&quot;524&quot;>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!</span></p> <p><span data-markdown-start-index=&quot;1050&quot;>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.</span></p> <p><span data-markdown-start-index=&quot;1620&quot;>This role is based in our Menlo Park, CA, or Washington, DC office(s), with in-person attendance expected at least 3 days per week.&nbsp;</span></p> <p><span data-markdown-start-index=&quot;1793&quot;>At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. </span><span data-markdown-start-index=&quot;1947&quot;>Our office experience is intentional, energizing, and designed to fully support high-performing teams. </span></p> <hr> <h2><span data-markdown-start-index=&quot;2083&quot;>What you ll do</span></h2> <ul> <li> <p><span data-markdown-start-index=&quot;2100&quot;>Build credit risk models for customer acquisitions (credit approval and credit limit assignment) and customer management</span></p> </li> <li> <p><span data-markdown-start-index=&quot;2222&quot;>Collaborate with credit analysts and product managers to understand the business problem and deliver appropriate models to solve them</span></p> </li> <li> <p><span data-markdown-start-index=&quot;2357&quot;>Create rich datasets leveraging both traditional and non-traditional data sources. Work on innovative tools to generate powerful features that boost models</span></p> </li> <li> <p><span data-markdown-start-index=&quot;2514&quot;>Deploy, maintain and monitor models in production</span></p> </li> <li> <p><span data-markdown-start-index=&quot;2565&quot;>Communicate with senior stakeholders in credit, product and engineering to deliver key results and findings</spa

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