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Director, Quantitative Analysis - Model Risk Office

Capital One, Mc Lean, VA, United States


* Strong understanding of quantitative analysis methods in relation to financial institutions.* Demonstrated track-record in model development and/or validation.* Experience utilizing model estimation tools.* Ability to clearly communicate modeling results to a wide range of audiences.* Drive to develop and maintain high quality and transparent model documentation.* Strong written and verbal communication skills.* Strong presentation skills.* Appreciation for processes, controls, and good governance.* Ability to manage complex projects that require cross-team collaboration.* A Master’s degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 7 years of experience in quantitative analytics* A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 4 years of experience in quantitative analytics- At least 7 years of experience in each of the following skills through education or experience:

* Statistical or econometric modeling

* Linear and logistic regression

* Programming in R, Python, or SQL

* Presenting statistical concepts and research results to non-statistical audience- At least 7 years of experience in at least 3 of the following skills:

* Survival analysis modeling

* Time-series analysis

* Panel data (longitudinal data or cross-sectional time-series data) analysis

* Cross-sectional data analysis

* Machine learning

* Analysis and management of large datasets (>1M records)* 8 years of experience in Python, Scala, R or other statistical analyst software* 8 years of experience with machine learning* 3 years of experience managing peopleCapital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. #J-18808-Ljbffr