Principal Quantitative Analyst - Model Risk Office

Capital One•McLean, VA

1d•$161,800 - $184,600•Onsite

About The Position

At Capital One, data is central to 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 Quantitative Analyst within the Model Risk Office, you will be part of the validation team responsible for balance sheet forecasting and economic stress test models used to determine capital requirements for Capital One's balance sheet items, including commercial and consumer loans. 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.

Requirements

Deep understanding of quantitative modeling in finance include but not limited to, financial time series model, interest rate term structure, Mortgage Prepayment Model, Market Risk Model, capital markets and investment portfolio modeling principles.
Extensive experience in Python or other object-oriented languages such as C++.
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.
Ability to fully own the model validation process: from conceptualization through data exploration, model selection, benchmarking, deployment, replication, independent testing, effective challenging, and performance monitoring. Able to communicate and socialize with various audience stakeholders about potential findings such as model user, model owner, and model developers
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 in quantitative analytics OR A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
At least 3 years of experience in each of the following skills through education or experience: Statistical or econometric modeling, Linear and logistic regression, Programming in R, Python or SQL, Presenting statistical concepts and research results to non-statistical audience
At least 3 years of experience in at least 3 of the following skills: Survival analysis modeling, Time-series analysis, Panel data (longitudinal data or cross-sectional time-series data) analysis, Cross-sectional data analysis, Machine learning, Analysis and management of large datasets (>1M records)

Nice To Haves

4 years of experience with Python, R or other statistical analyst software
4 years of experience manipulating and analyzing large data sets

Responsibilities

Partner with the various lines of business to develop and enhance Capital Markets modeling and analytical framework, such as deposit predictions, derivatives models and fixed income models.
Work across Capital One entities to create novel analytical solutions to challenging business problems.
Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.
Collaborate in a cross-disciplinary team to build cloud-based solutions grounded in data.
Identify opportunities to apply quantitative methods or machine learning to improve business performance.
Apply deep expertise in mathematics, statistical and machine learning methods to generate critical insights and decision frameworks for our business and customers.
Providing technical guidance to business leadership.
Communicate technical subject matter clearly and concisely to individuals from various backgrounds.
Understand and navigate Risk Management Software to enable business analysis.