Credit Data Science Manager II

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.

We are seeking an experienced Credit Data Science Manager II to lead the design, development, and oversight of credit risk models that drive decision-making, forecasting, and regulatory compliance. This role offers the opportunity to blend advanced data science expertise with business strategy, shaping portfolio performance and strengthening risk management practices. You will work closely with cross-functional teams and stakeholders, providing technical leadership while ensuring models align with organizational objectives and regulatory standards. This is a high-impact position, ideal for professionals who thrive in innovative environments and want to contribute to scalable, data-driven financial solutions.

Accountabilities:

• Oversee the development, validation, and performance monitoring of credit risk models used for decision-making, forecasting, and regulatory reporting.
• Apply advanced statistical, econometric, and machine learning methods to credit loss forecasting and credit decisioning.
• Manage model governance frameworks, ensuring compliance with regulatory requirements and internal standards.
• Provide leadership in stress testing, CECL implementation, scenario analysis, and loss forecasting methodologies.
• Ensure data integrity, quality, and management of inputs/outputs for all models.
• Collaborate with stakeholders, regulators, and auditors to present model results and provide insights.
• Develop and implement policies and procedures for monitoring and validating models while aligning with strategic goals.
• Lead projects from planning through execution, ensuring effective reporting and follow-up on action plans.

• 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 in Expected Loss modeling (PD, LGD, EAD) and experience with CECL, CCAR, DFAST, or IFRS 9 frameworks.
• Proficiency with statistical and programming tools such as Python, R, SAS, or SQL.
• Strong background in consumer credit products, including credit cards, loans, and unsecured lending.
• Familiarity with machine learning and statistical techniques for scorecards and origination underwriting models.
• Experience in modeling oversight, governance, or validation functions within banking, consulting, or financial services.
• Strong analytical, problem-solving, and communication skills, with the ability to present to both technical and non-technical stakeholders.
• Ability to thrive in a dynamic, fast-paced environment with strong leadership and team collaboration skills.

• Competitive salary range: $130,145 – $162,681 annually.
• Comprehensive healthcare, dental, and vision coverage.
• Generous paid time off and flexible work environment.
• Retirement plans and financial wellness support.
• Opportunities for professional growth and continuous learning.
• Inclusive workplace culture with strong emphasis on teamwork, innovation, and development.

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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