Manager, Quantitative Analysis - Global Finance

Capital One•McLean, VA

31d

About The Position

Manager, Quantitative Analysis - Global Finance 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.

Requirements

Deep understanding of quantitative modeling in relation to finance, deposit behaviors, capital markets 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.
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 development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.
Deep understanding of stochastic calculus, partial differential equations (PDEs) and monte carlo simulations.
Proficient in developing and implementing yield curve term structure models (e.g., Hull-White, Black-Karasinski) and multi-factor models (e.g., HJM or LMM) to capture the complexities of the term structure.
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 6 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 4 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) plus 1 year of experience performing data analytics

Nice To Haves

PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related disciplines.
5+ years of experience in statistical modeling, regression analytics or machine learning.
4+ years of credit risk modeling experience for commercial banks (default probability, loss given default, or exposure at default.)
2+ years of experience managing a team of analysts.

Responsibilities

Develop, implement, and calibrate term structure models. This role involves creating robust frameworks to accurately capture interest rate dynamics, enabling precise pricing of fixed-income instruments and effective risk management.
Calibrate model parameters by developing algorithms that achieve the "best fit" to the current volatility surface using market data, including swaps, bonds, and swaptions.
Integrate yield curve models across various lines of business to support finance organization modeling use cases, including interest rate risk analytics and stress testing.
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.