Credit Risk Data Scientist II

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Coastal is at the forefront of modern banking, combining strong financial infrastructure with cutting‑edge Banking-as-a-Service (BaaS) and fintech enablement strategies. We support not only individuals with their personal banking needs; we also empower businesses by integrating modern banking technology that drives growth, flexibility, and innovation.

At Coastal, we think and move like entrepreneurs; focused on impact, speed, and continuous improvement. We believe in working smart, collaborating deeply, and building solutions that unlock real potential. If you’re someone who thrives in a fast-moving environment, loves solving complex problems, and wants to help shape the future of banking, we’d love to meet you.

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Responsible for managing and overseeing the development, validation, and performance of internal credit risk models for the CCBX consumer portfolios and providing oversight of CCBX Partner models. These models are critical for credit loss forecasting, credit decisioning, risk management, and regulatory compliance. The role requires technical expertise, strong analytical skills, and deep knowledge of credit risk modeling, data science, and regulatory requirements.

• Proficiently oversees the development, validation, and performance monitoring of all credit risk models used for decision-making, risk management, or regulatory reporting.
• Shows expert level proficiency in credit risk modeling & data science techniques (e.g., statistical, econometric, or machine learning methods) used for loss forecasting, credit decisioning, and other use cases.
• Proficiency in statistical software and tools such as R, Python, SAS, SQL, or other modeling and data analysis platforms.
• Has a strong business knowledge of consumer credit, including credit cards, personal loans, unsecured lending
• Manage the model governance framework, ensuring effective control processes for model development, production, and validation.
• Strong understanding of CECL standard, stress testing methodologies, loss forecasting, and macroeconomic scenario analysis.
• Analytical and problem‑solving skills to assess model performance, conduct back testing, and make adjustments when necessary.
• Has a strong working knowledge of data management, data integrity, and data quality issues related to model inputs and outputs.
• Ability to develop and implement policies and procedures to monitor and validate credit risk models.
• Strong communication and presentation skills to interact with internal and external stakeholders, including senior management, auditors, and regulators.
• Ensure that credit modeling & data science activities align with the organization’s risk appetite and strategic goals.
• Provides leadership during project planning, execution, reporting, and follow‑up on any action plans.
• Proven experience with credit risk modeling for EL (Expected Loss) using the PD (Probability of Default), LGD (Loss Given Default), and EAD (Exposure at Default) modeling framework.
• Experience in modeling oversight, governance, or validation functions within banking, financial services, or consulting.
• Deep expertise with machine learning, data science, and statistical techniques for developing credit loss forecasting models for stress testing, CECL, CCAR, and other use cases is required.
• Experience with CECL process including modeling, implementation, and production of quantitative and qualitative components.
• Familiarity with machine learning, data science, and statistical techniques for developing credit scorecards & origination credit underwriting is highly desirable.
• Familiarity with Basel III, IFRS 9, CCAR, Dodd‑Frank, and other regulatory frameworks for credit risk models.
• Prior experience working…