Quantitative Analyst - Principal Associate

Capital One
Richmond, Virginia
Sep 14, 2021
Oct 14, 2021
Full Time
Locations: VA - Richmond, United States of America, Richmond, Virginia

Quantitative Analyst - Principal Associate

At Capital One data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Quantitative Analyst at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in cloud computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

As a Principle Associate of Quantitative Analysis within the Model Risk Office, you will be part of the validation team responsible for loss forecasting and economic stress test models used to determine loss reserves and capital requirements for retail portfolios including Credit Card and Auto lending. Validations cover all aspects of model development and performance and include forward-looking advancements in modeling capabilities and quality. With a network of over 500 quantitative analysts, data scientists and statisticians, we've created a dynamic environment with ample opportunities for learning and growth.

Responsibilities and Skills:
- Develop and execute validation testing for statistical, econometric, and machine learning models used in loss forecasting, allowance and stress testing for retail portfolios.

- Generate risk assessments and model insights based on validation evaluations and results.

- Develop alternative model approaches to assess model design and advance future capabilities.
- Understand relevant business processes and portfolios associated with model use.
- Communicate technical subject matter clearly and concisely to individuals from various backgrounds and roles both verbally and through written communication via model validation reports and presentations.
- Maintain the efficiency and accuracy of our models through ongoing model risk management and application of best practices.
- Remain on the leading edge of analytical technology and tools to identify areas of opportunity in our existing framework.

Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Successful candidates would possess:

- Demonstrated knowledge and track-record in statistical modeling.

- Experience utilizing model estimation tools such as R or Python.

- Ability to clearly communicate modeling results to a wide range of audiences.
- Strong written skills and ability to create and maintain high quality model documentation.

- Drive to continuously improve all aspects of their work in a collaborative fashion.

Basic Qualifications:
- Bachelor's Degree plus at least 5 years of experience in data analytics, or Master's Degree plus at least 3 years in data analytics, or PhD
- At least 1 years of experience in data analytics or financial modeling or econometric modeling (can include Graduate School Research work).
- At least 2 years of programming experience.

Preferred Qualifications:
- Master's Degree or PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.
- 1 year of experience with Python, R or other statistical analyst software
- 2 years of experience with data manipulation and analysis with large data sets
- Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, decision trees, machine learning methods)

Capital One will consider sponsoring a new qualified applicant for employment authorization for this po sition.