Director, Quantitative Analysis - Commercial Credit Modeling Team

Capital One•McLean, VA

12d

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. Capital One’s Counterparty Risk Group has a $7B+ credit risk portfolio toward Financial Institutions across the Enterprise. We also extend credit risk management to the Global Payment Network and the Commercial Bank at the intersection of financial institutions and commercial lending. In the Counterparty Risk Group, you’ll get an opportunity to solve a diverse set of problems with a diverse set of tools. In some settings, you’ll leverage open source programming or cloud computing to predict credit risk events across complex datasets using statistical techniques. In other settings, you’ll get the opportunity to use completely different skill sets, blending business insights with quantitative tools when forecasting rare or unprecedented events. It’s a team full of exciting opportunities to solve a range of complex problems, generating insights for credit decision makers.

Requirements

Strong understanding of quantitative analysis methods relating to financial institutions and financial risk exposures.
Demonstrated track-record in model development and/or validation.
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.
Appreciation for processes, controls, and good governance.
Ability to manage complex projects that require cross-team collaboration.
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 7 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) plus 4 years of experience in quantitative analytics
At least 7 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 7 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

8 years of experience in Python, Scala, R or other statistical analyst software
8 years of experience with machine learning
3 years of experience managing people

Responsibilities

Communicate clearly and concisely both verbally and through written communication via model validation presentations and reports and presentations.
Develop and implement strategies for statistical and financial models used to support Counterparty Credit Risk processes.
Assess the quality and risk of model methodologies, outputs, and processes.
Develop alternative approaches to model design and deployment capabilities.
Apply expertise in econometric, statistical, and machine learning methods to generate insights in modeled risks.
Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.