This role pays more than
89%
of similar roles. Most pay
$157,875–$214,975
— the shaded band above.
At the midpoint, this role pays about
$246k
versus about
$186k
for comparable roles.
Based on 238 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 Manager, Quantitative Analysis - Model Risk Office, you will join the Model Risk function to partner with development teams in advancing the Loan Loss Forecasting and Allowance for Credit Losses framework. You will be responsible for developing alternative model approaches to assess design, ensuring accuracy through continuous improvement, and creating high-quality documentation. The role involves identifying opportunities within existing frameworks using open source tools while communicating complex technical concepts to non-specialist audiences and senior management. To succeed, you must possess expertise in econometric theory, statistical modeling, and machine learning. You will utilize Python, R, or SQL to manage large datasets and perform linear/logistic regression, time-series analysis, and survival analysis. The work focuses on solving complex technical issues in econometric and statistical modeling to mitigate risks within the firm's financial reporting systems.
Develop alternative model approaches to assess design and advance future capabilities for loan loss forecasting and credit losses.
Identify areas of opportunity in existing frameworks using open source technologies and tools.
Maintain the efficiency and accuracy of models through continuous improvement and best practices.
Create high-quality, transparent documentation for all quantitative models and processes.
Communicate complex technical concepts and research results to non-specialist audiences and senior management.
Apply econometric, statistical, and machine learning skills to assess model risks and opportunities.
Translate business needs into technical requirements while managing large datasets and complex modeling tasks.
What we're looking for
Must have a Master's degree in a quantitative field plus 5 years of experience or a PhD in a quantitative field plus 2 years of experience.
Requires at least 5 years of experience in statistical or econometric modeling, linear and logistic regression, and programming in R, Python, or SQL.
Requires at least 5 years of experience in three areas: survival analysis, time-series analysis, panel data, cross-sectional data, machine learning, or large dataset management.
Must be able to present complex statistical concepts and research results to non-technical audiences.
Experience with machine learning and advanced analytical technologies is required.
Experience working with Agile development methodologies is required.
Preferred: 6 years of experience with Python, R, or other statistical analyst software.
Preferred: 6 years of experience in statistical modeling, regression analytics, or machine learning.
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