Principal Associate, Quantitative Analysis - Model Risk Management

Blackwomenintech; McLean (VA); On-site; USD 125,000 - 150,000; Full time

Job description

Center 2 (19050), United States of America, McLean, Virginia

Principal Associate, Quantitative Analysis - Model Risk Management

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 Principal Associate of Quantitative Analysis within the Model Risk Office, you will be part of the validation team responsible for loss forecasting, allowance, and stress testing (CCAR) 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:

1. Develop and execute validation testing for statistical, econometric, and machine learning models used in loss forecasting, allowance, and stress testing for retail portfolios.
2. Generate risk assessments and model insights based on validation evaluations and results.
3. Develop alternative model approaches to assess model design and advance future capabilities.
4. Understand relevant business processes and portfolios associated with model use.
5. 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.
6. Maintain the efficiency and accuracy of our models through ongoing model risk management and application of best practices.
7. 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 will possess:

1. Demonstrated knowledge and track record in statistical modeling.
2. Experience utilizing model estimation tools such as R or Python.
3. Ability to clearly communicate modeling results to a wide range of audiences.
4. Strong written skills and ability to create and maintain high-quality model documentation.
5. Drive to continuously improve all aspects of work in a collaborative fashion.
6. 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.

Basic Qualifications:

1. Currently has, or is in the process of obtaining a Bachelor's Degree plus at least 5 years of experience in data analytics, or currently has, or is in the process of obtaining a Master's Degree plus at least 3 years in data analytics, financial modeling, or econometric modeling (can include Graduate School Research work) or currently has, or is in the process of obtaining PhD with an expectation that required degree will be obtained on or before the scheduled start date.
2. At least 2 years of programming experience.

Preferred Qualifications:

1. PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.
2. 2+ years of experience with data analysis.
3. 1+ year of experience with Python, R or other statistical analyst software.
4. 1+ year of experience manipulating and analyzing large data sets.

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

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex (including pregnancy, childbirth or related medical conditions), race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state or local law.

Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [e-mail]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to [e-mail].