Credit Risk Analytics - Team Lead

Credit Risk Analytics - Team Lead page is loaded## Credit Risk Analytics - Team Lead locations:
New York, NY (1271 AOA/6th Ave) time type:
Full time posted on:
Posted 30+ Days Agojob requisition :
R6146
** Summary
* * Mizuho Americas Enterprise Risk Management is seeking a highly skilled Executive Director with extensive hands-on experience in Quantitative Credit Modeling to join our dynamic and high pace growing team in New York City. This role focuses on the quantitative model development, issue remediation, and maintenance of advanced credit risk models and risk analytics for the suite of wholesale credit risk models, ensuring alignment with regulatory requirements (e.g., CCAR/DFAST, CECL, Basel III/IV) and business objectives.

The candidate will possess strong technical expertise in quantitative modeling, a deep understanding of regulatory frameworks, and the ability to collaborate across risk, finance, and technology teams.

The candidate will manage the Credit Risk Analytics team, a division of the Risk Analytics Team, that partakes in model development over the full life cycle of models from methodology and design to local implementation and validation and will act as the technical expert setting up the vision and standards for credit risk modeling.
** Responsibilities
* ** Manage a team of 7 quantitative developers focused on specialized credit risk analytics supporting credit stress testing, CECL, and rating models.
* Cooperate with the business to design, develop, and calibrate credit risk models (such as PD, LGD, EAD) for wholesale and commercial portfolios, including stress testing (CCAR/DFAST), CECL, and Basel III/IV-compliant risk rating frameworks, as well as Advanced Internal Rating models for industry sectors and specific portfolios.
* Oversee ongoing support and refinement of existing suite of credit models; develop methodology & algorithms for new models and analytics.
* Perform ongoing model monitoring testing, and sensitivity analysis to ensure robustness across economic cycles.
* Manage the implementation of model changes, enhancements and remediation plans using advanced statistical tools and techniques.
* Work with stakeholders across business and functional teams, including Model Risk Management, during model development, validation, and implementation to ensure rigorous testing, well-documented methodologies, and compliance with internal and regulatory requirements.
* Define standards and work with IT business partners on model execution platform development and maintenance of existing code for Credit Risk models.
* Support internal and external remediation, regulatory submissions discussions with regulators and control functions as a subject matter expert, and address feedback from examiners (e.g., SEC, FRB), and Internal Audit.
* Stay current with advancements in credit risk modeling (e.g., AI/ML applications).
* Benchmark against industry best practices and peer institutions.
* Train, develop and supervise a team of quantitative analysts.
** Qualifications
* ** 10+ years of experience in credit risk modeling, with a focus on wholesale portfolios at large(s) financial institution(s).
* 3+ years of experience managing a team of credit risk quants.
* Master’s or PhD in Quantitative Finance, Statistics, Mathematics or related field.
* Deep knowledge of credit risk modeling for wholesale and corporate portfolios with practical implementation experience, including experience with Moody’s suite of models (CMM, EDF, Portfolio Analyzer).
* Track record in developing regulatory-compliant models (CCAR, CECL, Basel III/IV).
* Experience with large datasets and familiarity with banking products (e.g., corporate loans, CRE, leveraged finance).
* Experience with programming languages (Python, R, SAS, Stata), SQL databases, and data visualization tools (Power

BI, Tableau).
* Experience supervising code development/update and integration into an analytical platform.
* Expertise in statistical methods: regression analysis, time series forecasting (e.g., Bayesian inference, tools for regime switching), machine learning (e.g., XGBoost, Tensor Flow).
* Knowledge of credit risk platforms (e.g., Moody’s EDF, Risk…