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. Experience with risk machine learning models is a plus.

Responsibilities

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.

Requirements

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: Familiarity with building and deploying machine learning models for risk assessment.
Experience in SaaS or FinTech environments: Prior experience working in fast-paced, tech-driven environments with a focus on financial services or SaaS is beneficial.