This role pays more than
59%
of similar roles. Most pay
$119,050–$173,200
— the shaded band above.
At the midpoint, this role pays about
$145k
versus about
$146k
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
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Senior Associate, Quantitative Analyst - Commercial Credit Modeling Team joins the Commercial Bank Team to develop credit risk models for internal rating, loss forecasting, stress testing, and pricing for Structured Products. This role involves leveraging open-source programming and cloud computing to predict credit risk across multi-million record datasets using statistical techniques, while also utilizing simulation and quantitative tools to forecast rare events. The position requires proficiency in Python, R, and SQL to build accurate code and manage large datasets. Key responsibilities include collaborating with business analysts to enhance frameworks, partnering with infrastructure teams for cloud-based deployment, and ensuring model governance. Candidates must possess expertise in linear and logistic regression, time-series analysis, survival analysis, and machine learning. The role addresses complex problems within the commercial bank lending domain, specifically focusing on credit risk management and portfolio analytics.
Develop credit risk models for internal rating, loss forecasting, stress testing, and pricing of structured products.
Use statistical techniques and machine learning to predict credit risk across multi-million record datasets.
Apply simulation methods and stochastic processes to forecast rare or unprecedented financial events.
Create accurate, efficient, and organized code using Python or R to automate processes and improve model performance.
Build cloud-based solutions for the deployment, monitoring, and maintenance of quantitative models.
Produce high-quality, transparent documentation for all developed models to ensure compliance with governance standards.
Translate complex statistical concepts into clear narratives for presentations to non-technical 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 in programming using 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 three of the following: survival analysis, time-series, panel data, cross-sectional data, machine learning, or large dataset management.
Must possess excellent coding skills in Python and/or R to create accurate, efficient, and organized code.
Preferred candidates have 2 years of experience with Python, R, or other statistical analyst software.
Python
AWS
Machine Learning
SQL
Spark
H2O
Conda
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Big Data
Cloud Computing
Statistical Modeling