Principal Quantitative Analyst - Model Risk Office

Capital One•McLean, VA

About The Position

Principal Quantitative Analyst - Model Risk Office 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, Quantitative Analyst within the Model Risk Office, you will be part of the model validation team, working on the validation of CECL, CCAR stress testing models and Interest Rate and Liquidity Risk Management models. Validations cover all aspects of model development and performance and include forward-looking advancements in model sophistication and quality. You will enhance your technical and analytical skills, while also working closely with business leaders to influence business strategy. With a network of over 500 quantitative analysts and data scientists, we’ve created a dynamic environment with plenty of room for you to learn, grow, and realize your full potential. Responsibilities and Skills: Develop and implement validation strategies for models used to support CCAR stress testing process, including, statistical and financial models. Assess the quality and risk of model methodologies, outputs, and processes. Develop alternative model approaches to assess model design and advance future capabilities. Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights in assessing model risks and opportunities. Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations. Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies. 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: Strong understanding of quantitative analysis methods in relation to financial institutions. Demonstrated track-record in econometric analysis. Experience utilizing model estimation tools. Ability to clearly communicate modeling results to a wide range of audiences. Drive to develop and maintain high quality and transparent model documentation. Strong written and verbal communication skills. Strong presentation skills. Appreciation for processes, controls, and good governance. Ability to manage complex projects that require cross-team collaboration. Basic Qualifications: - Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: - A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics - A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics - A PHD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Requirements

Strong understanding of quantitative analysis methods in relation to financial institutions.
Demonstrated track-record in econometric analysis.
Experience utilizing model estimation tools.
Ability to clearly communicate modeling results to a wide range of audiences.
Drive to develop and maintain high quality and transparent model documentation.
Strong written and verbal communication skills.
Strong presentation skills.
Appreciation for processes, controls, and good governance.
Ability to manage complex projects that require cross-team collaboration.
Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
A PHD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Nice To Haves

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

Responsibilities

Develop and implement validation strategies for models used to support CCAR stress testing process, including, statistical and financial models.
Assess the quality and risk of model methodologies, outputs, and processes.
Develop alternative model approaches to assess model design and advance future capabilities.
Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights in assessing model risks and opportunities.
Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations.
Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.