This role pays less than
53%
of similar roles. Most pay
$123,375–$187,375
— the shaded band above.
At the midpoint, this role pays about
$145k
versus about
$155k
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.
As a Senior Associate, Quantitative Analyst - Model Risk Office, you will join the Model Risk function to partner with development teams and risk teams in advancing the Loan Loss Forecasting and Allowance for Credit Losses framework. You will collaborate across various lines of business to create novel analytical solutions, identify opportunities for automation, and build cloud-based solutions grounded in data. The role requires applying quantitative methods and machine learning to improve business performance and process efficiencies. You will utilize tools such as R, Python, or SQL to perform statistical modeling, including linear and logistic regression, time-series analysis, survival analysis, and panel data analysis. Key responsibilities include managing large datasets exceeding one million records and producing high-quality documentation for model validation while communicating complex results to diverse audiences within the financial institution context.
Partner with business lines to enhance modeling and analytical frameworks for loan loss forecasting and credit losses.
Develop novel analytical solutions to solve complex business problems across various Capital One entities.
Identify opportunities to apply quantitative methods and machine learning to improve business performance.
Implement automation solutions to increase operational efficiency and process improvements.
Build cloud-based solutions grounded in large datasets using Python, R, or SQL.
Create high-quality, transparent model documentation and validation reports.
Communicate complex modeling and validation results clearly to a wide range of audiences.
What we're looking for
Must have a Master's degree in a quantitative field or an MBA with a quantitative concentration plus 1 year of experience in quantitative analytics.
Must have at least 1 year of experience in statistical or econometric modeling.
Must have at least 1 year of experience in linear and logistic regression.
Must have at least 1 year of experience programming in R, Python, or SQL.
Must have at least 1 year of experience presenting statistical concepts to non-statistical audiences.
Must have at least 1 year of experience in at least three of the following: survival analysis, time-series, panel data, cross-sectional data, machine learning, or large dataset management.
Experience with Python, R, or other statistical analyst software (preferred).