This role pays more than
92%
of similar roles. Most pay
$168,750–$246,700
— the shaded band above.
At the midpoint, this role pays about
$288k
versus about
$208k
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
889 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 886 roles with salary data.
Director, Quantitative Analysis - Commercial Credit Modeling Team joins the Counterparty Risk Group to manage credit risk for financial institutions and commercial lending. This role involves developing and implementing strategies for statistical and financial models, assessing methodology quality, and creating automation solutions to improve business performance. The position requires building tools to predict credit risk events across complex datasets and forecasting rare or unprecedented events by blending business insights with quantitative methods. Candidates will utilize econometric, statistical, and machine learning techniques, including linear and logistic regression, survival analysis, time-series analysis, and panel data analysis. Technical proficiency is required in R, Python, SQL, and Scala to manage large datasets exceeding one million records. The role demands high-quality documentation and the ability to communicate complex modeling results clearly to diverse audiences while ensuring robust governance and process controls.
Develop and implement strategies for statistical and financial models to support Counterparty Credit Risk processes.
Assess the quality and risk of model methodologies, outputs, and internal processes.
Apply econometric, statistical, and machine learning methods to generate insights regarding modeled risks.
Identify opportunities to use quantitative methods and automation solutions to improve business performance and efficiency.
Create high-quality, transparent documentation for all models and technical processes.
Communicate complex modeling results and research findings clearly to both technical and non-technical audiences.
Manage complex projects involving cross-functional teams and diverse data sets.
What we're looking for
Must have a Master's degree in a quantitative field plus 7 years of experience or a PhD in a quantitative field plus 4 years of experience.
Must have at least 7 years of experience in statistical or econometric modeling, linear and logistic regression, and programming in R, Python, or SQL.
Must have at least 7 years of experience in at least three of: survival analysis, time-series, panel data, cross-sectional data, machine learning, or large dataset management.
Must be able to communicate statistical concepts and research results clearly to non-statistical audiences.
Must possess a track record in model development and/or validation for financial risk exposures.
8 years of experience in Python, Scala, R, or other statistical analyst software (preferred).
8 years of experience with machine learning (preferred).
3 years of experience managing people (preferred).