Principal Quantitative Analyst - Global Finance

Capital One•McLean, VA

About The Position

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. This position offers a unique opportunity to be a part of the dynamic Finance organization at Capital One. The Finance organization has been on a technology journey seeking to find ways to leverage technology to drive deeper insights and make the complex simple. We are looking for candidates to help in our journey with modeling, analytical and/or model implementation skills to join our finance team. Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Requirements

Deep understanding of quantitative modeling in relation to finance, capital markets, derivatives and investment portfolio modeling principles.
Extensive experience in Python or other object-oriented language.
Ability to clearly communicate modeling results to a wide range of audiences.
Strong written and verbal communication skills.
Strong presentation skills.
Currently has, or is in the process of obtaining one of the following with an exception that the required degree will be obtained on or before the scheduled start date: 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
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 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.
Drive to develop and maintain high quality and transparent model documentation.
Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.