Model Development & Decision Science Director

About the Role: Responsible for advancing the organization’s quantitative modeling, predictive and prescriptive analytics, model performance monitoring, decision science and AI/ML capabilities. Responsible for strengthening consistency and transparency in modeling practices and guide the responsible use of artificial intelligence and other emerging technologies in model development and decision science. Partners closely with business, technology, risk, compliance, and operations stakeholders to deliver scalable, well-controlled, and production-ready analytics solutions that improve decisioning, operational efficiency, risk management, and business performance. Regularly briefs executive management and risk/governance forums on model development, AI/ML initiatives, platform modernization, and delivery risks, and serves as a critical leader within the enterprise AI Center of Excellence by helping shape responsible AI adoption, governance, and scalable implementation practices. Accountable for predictive models, forecasting methodologies, optimization engines, machine learning models, AI-enabled decisioning and associated model lifecycle management. Lead the 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 across credit risk, fraud, finance, treasury, compliance, and other enterprise use cases. Define modeling standards, performance monitoring expectations, model documentation requirements, MLOps practices, best practices, and enabling technologies to strengthen analytical quality, transparency, and forecasting capabilities Lead a multi-level model development and decision science organization, with responsibility for 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 to oversee the full 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 predictive analytics, quantitative modeling, decision science, and AI solutions aligned with enterprise goals. Serve as the 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 in support of the enterprise AI Center of Excellence agenda, including development and deployment of controlled, explainable, auditable AI/ML and automation solutions that improve decisioning, efficiency, risk management, and business performance. This includes ownership of 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 for quantitative, forecasting, and AI/ML 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. Demonstrates compliance with all bank regulations for assigned job function and applies to designated job responsibilities – knowledge may be gained through coursework and on-the-job training. Keeps up to date on regulation changes. Follows all Bank policies and procedures, compliance regulations, and completes all required annual or job-specific training. Maintain a working knowledge of Bank's written policies and procedures regarding Bank Secrecy Act, Regulation CC, Regulation E, Bank Security and other regulations as applicable to this job description. May be asked to coach, mentor, or train others and teach coursework as subject matter expert. Actively learns, demonstrates, and fosters the Columbia corporate culture in all actions and words. Takes personal initiative and is a positive example for others to emulate. Embraces our vision to become “Business Bank of Choice” May perform other duties as assigned. About You: Bachelor’s Degree. Required. Advance Degree in Financial Engineering, Applied Mathematics, Statistics, Economics, Finance, Risk Management or equivalent work experience. Doctoral degree preferred. 7-10+ years in statistical modeling or model implementation, preferably in the banking or finance industry. Required 4-7+ years Experience leading quantitative modeling, data science, or analytics teams, including experience managing managers or senior technical leads in a regulated banking or financial services Required 3+ years Experience with Commercial Real Estate Risk Modeling Preferred 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. Demonstrated capability to set organizational direction, manage a broad portfolio of model development, monitoring, AI/ML, and decision science initiatives, and align delivery 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 that improve decisioni