Senior Associate, Quantitative Analyst - MLRO

Overview

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.

Responsibilities and Skills

• 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
• Provide technical guidance to business leadership
• Communicate technical subject matter clearly and concisely to individuals from various backgrounds.

Basic Qualifications

• 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)

Preferred Qualifications

• 2 years of experience with Python, R or other statistical analyst software