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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 works on developing market and counterparty credit risk models for the Capital Markets line of business. These models are critical to enabling continued prudent growth of the Commercial Bank and other trading activity. The outputs of these models will be used in hedging trading risks, stress testing, risk-weighted assets, and limits monitoring.
Job Responsibility:
Partner with the various lines of business to enhance modeling and analytical framework. Particularly focused on Value-at-Risk (VaR), Greeks (sensitivities), Potential Future Exposure (PFE), and Credit Value Adjustment (CVA)
Work across Capital One entities to create novel analytical solutions to the 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 to improve business performance
Providing technical guidance to business leadership
Communicate technical subject matter clearly and concisely to individuals from various backgrounds
Requirements:
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 1 year of experience in quantitative analytics
At least 1 year 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 1 year 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 have:
2 years of experience with Python, R or other statistical analyst software