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Tier-1 brand, mid-level data role, metro location, and common skillset raise competition.
High due to specialized commercial credit and underwriting expertise limiting industry transferability.
High due to explicit years and specialized credit risk domain requirement.
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Develop, own, and manage credit risk data and predictive models driving risk strategies for Rippling's financial products such as Corporate Card, Bill Pay, Payroll, and Employer of Record.
Analyze large datasets including bank transactions and payroll data to identify credit risk, concentration risks, and design actionable risk mitigation strategies.
Collaborate with Credit Strategy, Product, and Engineering teams to align analytics with business goals and measure the effectiveness of credit management strategies through KPI reporting.
3-6 years of experience in data science and analytics focused on risk challenges, preferably in financial technology, payments, or SaaS.
Proficiency in data analysis tools including Python, R, SQL, and experience creating risk detection strategies from large datasets.
Strong knowledge of commercial credit risk, including bank underwriting, financial statement analysis, or insurance premium setting.
Bachelor's degree in Data Science, Mathematics, Statistics, Operations Research, or a related field; Master's degree preferred.
Experienced in developing and evolving credit risk machine learning models or advanced indexes aligned to new product launches and risk strategies.
Able to synthesize complex financial data into actionable strategies balancing risk reduction and customer experience.
Effective collaborator across multi-disciplinary teams (Credit, Product, Engineering) within fast-paced SaaS or FinTech environments.