Credit Data Scientist & AI Agents

About Rippling Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system. Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds. Based in San Francisco, CA, Rippling has raised $1. 8B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes. We prioritize candidate safety. Please be aware that all official communication will only be sent from @ Rippling.com addresses. About the role As a Credit Risk Data Scientist on the Financial Risk Data Science team at Rippling, you will play a key role in leveraging advanced analytics and data-driven insights to identify, assess, and mitigate credit risks across our financial products. The primary focus of this role is to develop, own, and manage data and models that drive risk strategies across Rippling products, such as Corporate Card, Bill Pay, Payroll, and Employer of Record. Experience with risk machine learning models is a plus. What you will do Develop data-driven credit management strategies : Work with the Credit Strategy team to use advanced analytics in designing and enhancing strategies that identify high-risk indicators within a population and across multiple financial products, including credit limit assignment, onboarding, and deterioration over time. Analyze financial health patterns and risk trends : Perform deep analysis on bank transactions, payroll, and other data sources to score customer credit risk, identify concentration risks, and translate these findings into actionable risk mitigation strategies. Collaborate across teams : Work closely with Credit, Product, and Engineering teams to align data initiatives with business goals, ensuring that analytics-driven decisions are integrated into product development and operational workflows. Measure success and adjust strategies : Manage KPI reporting on credit strategies and collaborate with stakeholders to set and deliver against ambitious targets. Own credit risk data structures : Assemble data from internal and external sources into organized structures and define features that power credit risk management strategies. Develop new credit risk models : Evolve existing indexes and predictive models, and develop extensions or new models to support new strategies and product launches. What you will need 3-6 years of experience in data science and analytics : Demonstrated experience using analytics and data science techniques to solve risk-related challenges, particularly in financial technology, payments, or SaaS industries. Expertise in data analysis : Proficient in extracting insights from large datasets, with hands-on experience using tools such as Python, R, SQL, and other data analysis platforms to create robust risk detection strategies. Strong knowledge of credit risks : Deep understanding of commercial credit, including areas such as bank-based underwriting, financial statement analysis, and/or insurance premium setting. Data-driven approach to decision-making : Experience in developing data-driven strategies that address risks while balancing the impact on customer experience and operational efficiency. Effective cross-functional collaboration : Proven ability to collaborate with product, risk, and engineering teams to drive fraud risk initiatives. Educational background : Bachelor's degree in a relevant field such as Data Science, Mathematics, Statistics, or Operations Research. A Master’s degree is preferred. Nice to have Experience with machine learning models : Familiari