This role pays more than
82%
of similar roles. Most pay
$157,104–$214,900
— the shaded band above.
At the midpoint, this role pays about
$230k
versus about
$186k
for comparable roles.
Based on 239 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Manager, Quantitative Analysis - Model Risk Office joins the Model Validation team within the Model Risk Office to ensure the accuracy and robustness of market risk models. This role involves validating models used for derivative pricing and risk management, including counterparty risk and derivative valuation. The individual will develop model approaches to assess design, manage technical issues in econometric and machine learning modeling, and maintain high-quality documentation while utilizing open source tools. Key responsibilities include communicating complex technical concepts to non-specialist audiences and stakeholders like regulators and senior management. Required skills include proficiency in Python, R, or SQL, as well as experience with linear and logistic regression, time-series analysis, survival analysis, and large dataset management. The role specifically addresses the business problem of managing risk within derivative modeling and meeting CCAR regulatory requirements through advanced quantitative analysis.
Validate market risk models, specifically those used for derivative pricing, valuation, and counterparty risk.
Assess model design and develop new approaches to improve future analytical capabilities.
Identify technical issues in econometric, statistical, and machine learning modeling to evaluate risks and opportunities.
Translate complex technical concepts into clear reports and presentations for non-specialist audiences and senior management.
Maintain the accuracy of models through continuous improvement and the application of best practices.
Develop and maintain high-quality, transparent documentation for all model risk activities.
Utilize open-source technologies and tools to identify areas of opportunity within existing frameworks.
What we're looking for
Must have a Master's degree in a quantitative field plus 4 years of experience or a PhD in a quantitative field plus 1 year of experience.
Requires at least 4 years of experience in statistical/econometric modeling, linear and logistic regression, and programming in R, Python, or SQL.
Must have at least 4 years of experience in three areas including survival analysis, time-series, panel data, cross-sectional data, machine learning, or large dataset management.
Requires a proven track record in modeling and using tools like Python or R to communicate results to non-technical audiences.
Preferred: 5 years of experience with Python, R, or other statistical analyst software.
Preferred: 5 years of experience in statistical modeling, regression analytics, or machine learning.
Preferred: At least 2 years of experience in derivative modeling including fixed income, commodity, FX, or CDS.
Experience with CCAR regulatory requirements and Agile development methodologies is required.
Machine Learning
Python
Scala
R
AWS
Relational Databases
Gradient Boosting
Deep Learning
Clustering
Classification
Sentiment Analysis
Big Data
Statistical Modeling
econometric, s
open-source tools