Credit Data Science Manager II

Job Type Full-time Description

ABOUT US

Coastal Community Bank is a leader in Banking with strong Financial infrastructure leading Banking as a Service (BaaS) and Fintech strategies. The people at Coastal not only help people with their personal banking; they help businesses with banking technology integration that leads to business growth, flexible financing, and unimagined potential. We think and work like entrepreneurs, always moving and constantly improving. We are go-getters, work hard, and play hard. If you're someone who thrives on innovation, wants to help others succeed, knows how to think outside the box, and believes that we're all in this together -- you belong here.

OVERVIEW

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.

Requirements

RESPONSIBILITIES TO INCLUDE

• 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.

QUALIFICATIONS

• 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 with regulatory stress testing models (e.g., CCAR/DFAST) or impairment models (e.g., IFRS 9).

EDUCATION/EXPERIENCE

• Bachelor’s degree in Quantitative Finance, Economics, Statistics, Mathematics, Data Science, Computer Science, or a related field.
• Master’s degree in a quantitative discipline (e.g., Financial Engineering, Data Science, Applied Mathematics) is preferred.
• 7+ years of experience in credit risk modeling, model development, validation, or model risk management, and forecasting techniques
• Prior experience with Machine learning, data science, and statistical modeling.

HOW YOU’LL THRIVE AT COASTAL

• Be the Best – Communicate effectively, pay close attention to detail, and prioritize your personal development.
• Be Relentless – Thrive in a goal-oriented environment exercising both patience and persistence. Advocate for our customers and team members a