Model Development & Decision Science Director

Columbia Bank•Lake Oswego, OR

1d•Onsite

About The Position

The Model Development & Decision Science Director advances the organization’s quantitative modeling, predictive and prescriptive analytics, model performance monitoring, decision science, and AI/ML capabilities. The role strengthens the consistency and transparency of modeling practices and guides the responsible use of artificial intelligence and emerging technologies. It partners with business, technology, risk, compliance, privacy, operations, and data management stakeholders to deliver scalable, controlled, production-ready analytical solutions that improve decisioning, operational efficiency, risk management, and business performance. The Director is accountable for predictive models, forecasting methodologies, optimization engines, machine learning models, AI-enabled decisioning, and associated model lifecycle management across credit risk, fraud, finance, treasury, compliance, and other enterprise use cases. The role sets modeling standards, documentation and performance-monitoring expectations, MLOps practices, best practices, and enabling technology requirements. It leads a multi-level organization, sets portfolio priorities, and partners with Model Risk Management on validation, monitoring, recalibration, redevelopment, remediation, and governance compliance. The position is the senior point of accountability for regulatory, governance, examination, documentation, and executive reporting related to models and AI/ML solutions. It also leads AI innovation and automation delivery in support of the enterprise AI Center of Excellence, including controlled, explainable, auditable AI/ML and automation solutions. Enterprise business intelligence reporting, self-service analytics platforms, semantic models, enterprise KPI reporting, data engineering, and data governance remain the responsibility of the Data Management & Analytics function. The Director must comply with applicable bank regulations, policies, procedures, and required training; maintain working knowledge of relevant regulations, including Bank Secrecy Act, Regulation CC, Regulation E, and Bank Security requirements; and keep up to date with regulatory changes. The role may coach, mentor, or train others, and is expected to demonstrate and foster Columbia’s corporate culture and vision to become “Business Bank of Choice.” Other duties may be assigned.

Requirements

Bachelor’s degree required.
Seven to ten or more years of experience in statistical modeling or model implementation; banking or finance industry experience preferred.
Four to seven or more years of experience leading quantitative modeling, data science, or analytics teams, including managing managers or senior technical leads in regulated banking or financial services.
Expertise in quantitative modeling, credit portfolio modeling, CECL, stress testing, scenario analysis, statistical modeling, machine learning, predictive analytics, and AI/ML solution development in a regulated banking environment.
Ability to set organizational direction, manage a broad portfolio of model development, monitoring, AI/ML, and decision science initiatives, and align priorities with enterprise risk, regulatory, and business objectives.
Strong understanding of model risk management, data governance, regulatory expectations, explainability, auditability, privacy, controls, production monitoring, and responsible AI adoption.
Ability to translate complex business, regulatory, economic, and technical needs into practical, scalable, well-controlled analytical solutions.
Executive-level communication and influencing skills, including explaining complex quantitative, AI/ML, technology, and model risk topics to senior leaders, governance committees, auditors, regulators, and cross-functional stakeholders.
Ability to establish operating standards, documentation expectations, governance routines, and quality controls that support consistency, transparency, accountability, and effective model lifecycle management.
Leadership in organizational design, workforce planning, talent development, succession planning, performance management, and retention of specialized quantitative and data science talent.
Strategic thinking and demonstrated ability to lead change, foster innovation, adopt emerging modeling and technology tools, and partner across Business, Risk, Technology, Compliance, Privacy, and Data Management.
Technical fluency with Python, SQL, distributed data processing, cloud-based analytics platforms, MLOps practices, and tools such as Databricks, Snowflake, Spark, MLflow, scikit-learn, XGBoost, or similar technologies.
Experience advancing scalable model deployment, CI/CD automation, experiment tracking, performance monitoring, production governance, and technology controls with platform and engineering teams.

Nice To Haves

Advanced degree in Financial Engineering, Applied Mathematics, Statistics, Economics, Finance, Risk Management, or equivalent work experience.
Doctoral degree preferred.
Three or more years of experience with Commercial Real Estate Risk Modeling.

Responsibilities

Lead development, implementation, performance monitoring, and ongoing enhancement of quantitative models, predictive and prescriptive analytics, AI/ML methodologies, forecasting approaches, optimization models, and decision science solutions.
Define modeling standards, model documentation requirements, performance monitoring expectations, MLOps practices, best practices, and enabling technologies.
Lead and develop a multi-level model development and decision science organization, including talent development, succession planning, delivery quality, and team performance.
Set portfolio priorities for model development, monitoring, AI/ML, and decision science initiatives, balancing business value, regulatory expectations, resource capacity, execution risk, and production readiness.
Partner with Model Risk Management across the model lifecycle, including validation, monitoring, recalibration, redevelopment, remediation, and compliance with governance standards.
Partner with senior business, risk, compliance, privacy, technology, and data management leaders to identify, prioritize, resource, and deliver high-impact analytical and AI solutions aligned with enterprise goals.
Serve as senior point of accountability for regulatory, governance, examination, documentation, and executive reporting related to models and AI/ML solutions.
Lead AI innovation and automation delivery for the enterprise AI Center of Excellence, including controlled, explainable, auditable AI/ML and automation solutions; own business-facing AI/ML use cases, predictive models, and automated decisioning solutions.
Define model-development technology requirements, model deployment standards, experiment management practices, performance monitoring expectations, and MLOps standards.
Demonstrate compliance with applicable bank regulations, policies, procedures, and required training; maintain working knowledge of relevant regulations and keep up to date with regulatory changes.
Coach, mentor, or train others as needed and foster Columbia’s corporate culture and vision.
Perform other duties as assigned.

Benefits

Medical, dental, and vision plans for eligible associates.
401(k) retirement savings plan with employer match for qualifying associate contributions.
Employee assistance program.
Life insurance and disability insurance.
Tuition assistance.
Mental health resources.
Identity theft protection.
Legal support.
Auto and home insurance.
Pet insurance.
Access to an online discount marketplace.
Paid vacation, sick days, volunteer days, and holidays.
The role may be eligible for performance-based incentive compensation.