Director, Quantitative Analysis - Model Risk Office

Capital One•McLean, VA

2d•$244,700 - $307,200

About The Position

Director, Quantitative Analysis - Model Risk Office 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. As a Director, Quantitative Analysis within the Model Risk Office, you will be part of the model validation team, leading the validation and model risk strategies for Allowance (CECL) and CCAR stress testing models. Validations cover all aspects of model development and performance and include forward-looking advancements in model sophistication and quality. You will enhance your technical and analytical skills, while also working closely with business leaders to influence business and modeling strategies. With a network of over 500 quantitative analysts and data scientists, we’ve created a dynamic environment with plenty of room for you to learn, grow, and realize your full potential.

Requirements

Strong understanding of quantitative analysis methods in relation to financial institutions.
Demonstrated track-record in model development and/or validation.
Experience utilizing model estimation tools.
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

Develop and implement validation strategies for statistical and financial models used to support Allowance and CCAR stress testing processes.
Assess the quality and risk of model methodologies, outputs, and processes.
Develop alternative model approaches to assess model design and advance future capabilities.
Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights in assessing model risks and opportunities.
Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations.
Identify opportunities to apply quantitative methods and automation solutions to improve business performance and process efficiencies.