Sr. Director, Advanced Data Scientist

At FNBO, our employees are the heart of our story—and we’re committed to their success! Please see below the details of this career opportunity and how it fits into our organization’s success. Summary of the Job:
The Sr. Director, Advanced Data Scientist will provide senior strategic and technical leadership within the Partnership Division's data science and modeling function. This role is responsible for defining and executing multi-year modeling roadmap and overseeing the development and implementation of advanced machine learning models that support credit decisioning and portfolio management. The successful candidate will combine deep quantitative expertise with the organizational influence and executive presence required to shape strategy, drive cross-functional alignment, and deliver measurable business impact at scale.

Define and own the multi-year modeling roadmap for the development of several critical custom credit-risk acquisition and portfolio management models, establishing strategic priorities, sequencing, and investment rationale aligned with Partnership Division business objectives. Serve as a senior thought leader and decision-maker on modeling approaches, leading discussions with senior business, credit risk, first line of risk, compliance, and technology stakeholders to align methodology, priorities, and risk considerations.

Identify and evaluate emerging modeling capabilities, analytical approaches, and implementation methods that can expand the division's competitive advantage in credit decisioning and customer risk management. Communicate modeling strategy, project status, and key results to senior leaders through clear, executive-ready reporting — translating complex quantitative findings into actionable business insights and ensuring leadership has the visibility needed to make informed decisions.

Independently design and develop advanced ML models for credit card acquisition and underwriting and account management and customer risk. Apply advanced statistical and machine learning techniques to large-scale customer and credit datasets. Evaluate and implement algorithms including gradient boosting, random forests, neural networks, generalized additive models, ensemble methods, survival models, and other advanced ML approaches. Conduct fair lending analysis (e.g., least discriminatory analysis).

Identify opportunities to improve model performance, stability, interpretability, and business value.

Independently lead model-development projects from problem definition through production implementation. Translate business objectives into well-defined modeling frameworks, target variables, populations, and performance criteria. Perform feature engineering, variable selection, model development, hyperparameter optimization, and performance evaluation. Establish appropriate 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.

Conduct research on emerging ML techniques and assess their applicability to credit risk. Develop innovative approaches for extracting predictive signals from traditional and alternative data sources. Investigate nonlinear relationships, interactions, customer behavior patterns, and temporal dynamics that may not be captured by traditional credit-risk models. Develop interpretable ML approaches that balance predictive performance with regulatory and business requirements. Explore techniques such as explainable AI, causal modeling, ensemble…