This role pays more than
60%
of similar roles. Most pay
$152,825–$213,850
— the shaded band above.
At the midpoint, this role pays about
$192k
versus about
$183k
for comparable roles.
Based on 240 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
1043 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1037 roles with salary data.
Manager, Quantitative Analysis - Model Risk Audit joins the Model Audit team to review models and model risk practices across the enterprise while providing assurance to the Audit Committee. The role involves partnering cross-functionally with various business lines to develop innovative analytical solutions, identify automation opportunities, and create cloud-based solutions grounded in data. Key responsibilities include applying econometric, statistical, and machine learning methods to generate decision frameworks and maintaining high-quality documentation. Candidates must possess expertise in predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, and decision trees. The required technical toolkit includes proficiency in R, Python, or SQL for managing large datasets exceeding one million records. This role addresses the challenge of providing robust model risk oversight within a financial institution while leveraging advanced analytics to improve business performance and process efficiencies.
Apply econometric, statistical, and machine learning methods to generate critical business insights and decision frameworks.
Review models and model risk practices across the enterprise to provide assurance to the Audit Committee.
Develop novel analytical solutions to solve complex problems across various lines of business.
Identify opportunities to use automation and quantitative methods to improve process efficiencies.
Build cloud-based solutions grounded in large datasets using Python, R, or SQL.
Create high-quality, transparent documentation for all models and analytical processes.
Communicate technical subject matter clearly to non-technical audiences across the organization.
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.
Must have at least 4 years of experience in statistical or econometric modeling and linear/logistic regression.
Must have at least 4 years of experience in programming with R, Python, or SQL.
Must have at least 4 years of experience presenting statistical concepts to non-technical audiences.
Must have at least 4 years of experience in at least three of the following: survival analysis, time-series, panel data, cross-sectional data, machine learning, or large dataset management.
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.
At least 2 years of experience in derivative modeling (Fixed income, Commodity, FX, or CDS).