Sr. Director, Advanced Data Scientist

First National Bank of Omaha•Omaha, NE

4d•Onsite

About The Position

The Sr. Director, Advanced Data Scientist provides senior strategic and technical leadership for the Partnership Division’s data science and modeling function. The role defines and executes a multi-year modeling roadmap and oversees the development and implementation of advanced machine learning models for credit decisioning and portfolio management. It combines deep quantitative expertise with organizational influence and executive presence to shape strategy, align cross-functional stakeholders, and deliver measurable business impact. The position leads development of custom credit-risk models for acquisition, underwriting, account management, and customer risk. It owns model-development work from problem definition through production implementation, including framework design, feature engineering, model selection and optimization, evaluation, documentation, validation partnership, and monitoring. The role also researches emerging machine-learning methods, develops interpretable approaches suitable for regulatory and business requirements, and establishes best practices for model development, testing, documentation, and deployment. The incumbent is anticipated to work onsite at the posted location. Work location may change based on business needs.

Requirements

Master’s degree or PhD in Statistics, Data Science, Computer Science, Mathematics, Operations Research, Econometrics, or a related quantitative field.
At least 7 years of experience developing statistical or machine-learning models.
Strong experience independently developing predictive models using large and complex datasets.
Demonstrated expertise in Python and/or R and strong SQL skills.
Experience with gradient boosting, XGBoost or LightGBM, ensemble methods, neural networks, and other advanced machine-learning techniques.
Experience with model explainability techniques such as SHAP and partial dependence.
Experience with the full model-development lifecycle, including development, implementation, and monitoring.
Strong understanding of credit-risk concepts and consumer lending.
Unrestricted work authorization without a need for future sponsorship.

Nice To Haves

PhD, particularly with strong research experience in machine learning, statistics, or quantitative finance.
Experience developing models in financial services or consumer credit.
Experience developing credit card acquisition and account-management models.

Responsibilities

Define and own a multi-year modeling roadmap, including strategic priorities, sequencing, and investment rationale aligned with Partnership Division objectives.
Provide senior thought leadership on modeling approaches and align methodology, priorities, and risk considerations with business, credit risk, first line of risk, compliance, and technology stakeholders.
Identify and evaluate emerging modeling capabilities, analytical approaches, and implementation methods.
Communicate modeling strategy, project status, and results to senior leaders through executive-ready reporting and actionable business insights.
Design and develop advanced machine-learning models for credit card acquisition, underwriting, account management, and customer risk.
Apply advanced statistical and machine-learning techniques to large-scale customer and credit datasets, and evaluate algorithms including gradient boosting, random forests, neural networks, generalized additive models, ensemble methods, and survival models.
Conduct fair-lending analysis, including least discriminatory analysis, and identify opportunities to improve model performance, stability, interpretability, and business value.
Lead model-development projects from problem definition through production implementation, translating business objectives into modeling frameworks, target variables, populations, and performance criteria.
Perform feature engineering, variable selection, model development, hyperparameter optimization, and performance evaluation; establish training, validation, and out-of-time testing strategies.
Document modeling methodology, assumptions, limitations, and results; partner with model validation and model risk management teams to address challenges and incorporate feedback.
Research emerging machine-learning techniques and assess their applicability to credit risk; develop approaches for extracting predictive signals from traditional and alternative data sources.
Investigate nonlinear relationships, interactions, customer behavior patterns, and temporal dynamics; develop interpretable machine-learning approaches that balance predictive performance with regulatory and business requirements.
Explore explainable AI, causal modeling, ensemble learning, semi-supervised learning, and automated machine learning where appropriate.
Develop model performance and stability monitoring frameworks; monitor predictive performance, population shifts, variable stability, and model degradation.
Identify emerging risks and recommend model recalibration, redevelopment, or replacement when appropriate; conduct ongoing research to improve model performance and robustness.
Serve as a subject-matter expert in advanced machine learning and credit-risk modeling; establish best practices for model development, testing, documentation, and deployment.
Stay current with advances in machine learning, credit-risk analytics, and regulatory expectations.