Monitor credit risk models, including underwriting, loss forecasting, and fraud detection, and iterate based on observed portfolio performance
Design, build, and maintain scalable data pipelines, monitoring infrastructure, and dashboards to track portfolio health, user behavior, and key risk indicators
Partner with product, research, and engineering teams to define north star metrics and translate them into measurable, actionable credit and growth strategies
Design and analyze A/B tests, quasi-experiments, and causal inference studies to evaluate the impact of product and policy changes
Produce portfolio monitoring and investigative analyses, making recommendations based on findings
Translate complex quantitative findings into clear, compelling narratives for product, leadership, and cross-functional stakeholders

4+ years of experience in decision science, credit risk analytics, or a closely related quantitative role within fintech or consumer lending
Deep proficiency in Python and SQL; comfortable owning analyses end-to-end from raw data to recommendation
Strong understanding of credit risk modeling concepts, including PD/LGD modeling, scorecard development, reject inference, vintage analysis, and risk segmentation
Demonstrated experience monitoring credit risk metrics and portfolio performance, including loss forecasting and underwriting model improvement
Proven ability to influence and collaborate with cross-functional teams and senior stakeholders, with a track record of translating analytical findings into accessible, actionable insights
Experience designing and evaluating experiments (A/B tests, holdout groups, or causal inference frameworks) in a consumer product context
Comfortable with ambiguity and biased toward action; thrives with minimal oversight and brings strong problem-solving skills and sharp attention to detail

Divine

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