selffinancial · Austin, TX
Skills: Python, SQL
About Self Financial Self Financial is a venture-backed, high-growth FinTech company with a mission to increase economic inclusion and financial resilience by empowering people to build credit and build savings. We challenge the status quo of the credit industry by giving people accessible tools to take control of their credit, with a focus on the 100 million+ Americans who have no or low credit. We're looking for people who share our passion, tackle challenges, find solutions, and make the financial space better for the communities we serve. Title Staff Data Scientist Team Credit Analytics . Builds the predictive models that drive our credit and lending decisions. We turn deep knowledge of our data into models that are deployed in production, and we work closely with Risk, Finance, Product, and Engineering to improve how we underwrite, price, and manage our customers’ credit. Location: Austin, TX Work Arrangement: Austin – Hybrid or Remote About the Role You’ll help build the models that drive our credit and lending decisions, and learn how they get from a notebook into production. This is an entry-level role on a small team, so you’ll get real responsibility early and work closely with experienced data scientists and engineers. We care less about “here’s the model and here’s what it does” and more about what it changed for the business, the context behind it, and how clearly you explain it. What you’ll do: Build, test, and validate predictive models in Python under the guidance of senior data scientists Support the deployment of models into our decisioning engine and help monitor them after launch Dig into the data to understand why a model behaves the way it does, and keep asking questions until the answer holds up Help measure and communicate the business impact of your work Work with Risk, Finance, Product, and other stakeholders to understand business questions and turn them into analyses Learn the data vendors and tools we use and help evaluate them Take part…
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