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 managing complex technical issues in econometric, statistical, and machine learning modeling. You will utilize Python, R, or SQL to perform linear and logistic regression, time-series analysis, survival analysis, and panel data analysis on large datasets exceeding one million records. Success requires communicating complex research results to non-specialist audiences and regulators while navigating Agile development methodologies to solve critical financial risk problems within the banking domain.
Develop alternative model approaches to assess design and advance future capabilities for loan loss forecasting.
Identify and mitigate risks in econometric, statistical, and machine learning models.
Maintain the accuracy and efficiency of existing models through continuous improvement and best practices.
Create high-quality, transparent documentation for all quantitative models and processes.
Translate complex technical concepts into clear presentations for non-specialist audiences and senior management.
Utilize Python, R, or SQL to perform advanced statistical modeling and manage large datasets.
Leverage open-source technologies to identify opportunities within the existing risk framework.
What we're looking for
A Master's degree in a quantitative field or an MBA with a quantitative concentration plus 5 years of experience in quantitative analytics.
A PhD in a quantitative field plus 2 years of experience in quantitative analytics.
At least 5 years of experience in statistical or econometric modeling, linear and logistic regression, programming in R, Python, or SQL, and presenting results to non-statistical audiences.
At least 5 years of experience in at least three of the following: survival analysis, time-series, panel data, cross-sectional data, machine learning, or managing large datasets.
Experience with machine learning and Agile development methodologies.
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 (preferred).
2 years of experience managing people and 6 years of experience in models for Commercial Bank (preferred).
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