Credit Data Scientist & AI Agents

Role Overview

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.

Responsibilities

• Develop data-driven credit management strategies using advanced analytics for credit limit assignment, onboarding, and deterioration.
• Analyze financial health patterns and risk trends by performing deep analysis on bank transactions and payroll data.
• Collaborate across Credit, Product, and Engineering teams to align data initiatives with business goals.
• Manage KPI reporting on credit strategies and collaborate with stakeholders to deliver against targets.
• Own credit risk data structures and define features that power risk management strategies.
• Develop and evolve existing indexes and predictive models to support new product launches.

Requirements

• 3-6 years of experience in data science and analytics, particularly in FinTech, payments, or SaaS.
• Proficiency in extracting insights from large datasets using Python, R, and SQL.
• Deep understanding of commercial credit, bank-based underwriting, and financial statement analysis.
• Experience developing data-driven strategies that balance risk with customer experience.
• Bachelor's degree in Data Science, Mathematics, Statistics, or Operations Research (Master's preferred).

Nice to Have

• Experience with building and deploying Machine Learning models for risk assessment.
• Prior experience in fast-paced SaaS or FinTech environments.

Skills

• Python
• SQL
• R
• Machine Learning
• Data Analysis