Credit Data Science Manager II (Remote - US)

This position is posted by Jobgether on behalf of Coastal Community Bank. We are currently looking for a Credit Data Science Manager II in the United States.

This role offers the opportunity to lead the development, validation, and oversight of advanced credit risk models that directly influence credit decisioning, risk management, and regulatory compliance. You will manage a team of quantitative analysts and data scientists, ensuring that modeling frameworks are robust, accurate, and aligned with strategic business objectives. The position requires balancing technical rigor with stakeholder collaboration, providing insights that guide business strategy while maintaining regulatory standards. You will be at the forefront of applying machine learning and statistical methods to credit loss forecasting and risk assessment, working in a dynamic environment that values innovation, precision, and teamwork. Your leadership will shape model governance, data quality, and performance evaluation across multiple consumer credit portfolios.

Accountabilities:

·         Oversee the development, validation, and performance monitoring of credit risk models for consumer portfolios and partner models.

·         Apply statistical, econometric, and machine learning techniques to support loss forecasting, credit decisioning, and risk management.

·         Ensure model governance frameworks are implemented effectively, covering development, production, validation, and regulatory compliance.

·         Conduct back-testing, stress testing, and macroeconomic scenario analysis to assess model performance.

·         Collaborate with internal stakeholders, including senior management, auditors, and regulators, to communicate model results, risks, and recommendations.

·         Develop policies, procedures, and documentation to ensure data integrity, model accuracy, and alignment with organizational risk appetite.

·         Lead projects, providing guidance, monitoring progress, and ensuring timely delivery of high-quality modeling outputs.

·         Bachelor’s degree in Quantitative Finance, Economics, Statistics, Mathematics, Data Science, Computer Science, or related field; Master’s degree preferred.

·         7+ years of experience in credit risk modeling, model development, validation, or risk management.

·         Proven expertise with credit risk models using PD, LGD, and EAD frameworks.

·         Strong proficiency in statistical software and data analysis tools such as R, Python, SAS, and SQL.

·         Deep understanding of CECL, stress testing methodologies, loss forecasting, and macroeconomic scenario analysis.

·         Familiarity with regulatory frameworks including Basel III, IFRS 9, CCAR, Dodd-Frank, and related standards.

·         Strong analytical and problem-solving skills with attention to detail.

·         Excellent communication and presentation skills, capable of engaging technical and non-technical stakeholders.

·         Experience leading model governance, oversight, or validation functions is highly desirable.

·         Competitive salary range: $130,145 – $162,682 USD.

·         Comprehensive health, dental, and vision coverage.

·         Paid time off and holidays, with flexible scheduling options.

·         Professional development support and training opportunities.

·         Inclusive and collaborative workplace culture.

·         Opportunities for career advancement and leadership within the organization.

·         Retirement savings plan with employer contributions.

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

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