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Your day to day
Extract, prepare, and validate large, complex datasets from PayPal’s proprietary systems using SQL, Python to support model development and analytics.
Build, enhance, and document CECL/IFRS9-compliant loss prediction models, leveraging PD/EAD/LGD frameworks and vintage loss rate methodologies.
Conduct rigorous model performance testing, stress testing, and scenario analysis to ensure models are robust and regulatory-compliant
Collaborate cross-functionally with implementation, finance, accounting, risk, and external stakeholders to align on model outputs, definitions, and business needs.
Clearly communicate analytic insights and model results to senior leadership, and provide expert support for regulatory exams and audit reviews.
What do you need to bring
Advanced degree in a quantitative discipline (e.g., statistics, mathematics, data science, computer science, engineering, or related field).
Deep expertise in statistical modeling, machine learning, or econometrics applied to credit risk, knowledge and familiarity of CECL and IFRS9 regulatory requirements and modeling best practices is a plus.
Strong technical skills working with large datasets using SQL, Python, R, or similar tools; experience with cloud-based platforms such as GCP is highly desirable.
5+ years of hands-on experience developing consumer or small business credit risk models, preferably within a CECL/IFRS9 framework.
Excellent communication and collaboration skills, with strong business judgment and a passion for data-driven problem solving.
Our Benefits:
Any general requests for consideration of your skills, please
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