Sr. Director, Advanced Data Scientist

• 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 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 ML and credit-risk modeling.
• Establish best practices for ML model development, testing, documentation, and deployment.
• Stay current with advances in machine learning, credit-risk analytics, and regulatory expectations.

• Master's degree or PhD in Statistics, Data Science, Computer Science, Mathematics, Operations Research, Econometrics, or a related quantitative field.
• PhD strongly preferred, particularly for candidates with strong research experience in machine learning, statistics, or quantitative finance.
• 7+ years of experience developing statistical or machine learning models, preferably in financial services or consumer credit with experience developing credit card acquisition and account-management models.
• Strong experience independently developing predictive models using large and complex datasets.
• Demonstrated expertise in Python and/or R; strong SQL skills.
• Experience with gradient boosting/XGBoost/LightGBM, ensemble methods, neural networks, and other advanced ML 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.

This role may have a specific starting pay within this range.

Final compensation offer to candidate may vary from posted hiring range based upon work experience, education, and/or skill level.

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