Sr. Director, Advanced Data Scientist

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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.

About This Role:

1.  Vision and Strategy

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.

2.  Model Development

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.

3.  End-to-End Model Ownership

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.

4.  Advanced Analytics and Research

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.

5.  Model Performance and Monitoring

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.

6.  Technical Leadership

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.

The Ideal Candidate for This Role:

Required Qualifications

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.

Candidates must possess unrestricted work authorization and not require future sponsorship.

Work Environment:

It is anticipated that the incumbent in this role will work onsite at the posted location. Our onsite environment fosters innovation, mentorship, and a vibrant culture where ideas flow freely and relationships flourish. As part of our team, you'll experience the energy of our collaborative spaces designed to support your professional growth while working alongside talented colleagues who inspire excellence daily. Please note that work location is subject to change based on business needs.