Manager, Quantitative Analysis - Model Risk Office

Capital One•New York, NY

55d

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 is selectively recruiting for a Manager for a Model Validation team. The individual would report to the Model Risk Office and work closely with the business groups. This individual, along with their peers, would be responsible for ensuring the accuracy and robustness of the firm's market risk models. Clients of the group include senior management, business leads, internal audit, and the regulators. This position is responsible for validating models, specifically those used for derivative pricing and risk management, including derivative valuation, market risk, and counterparty risk models. Strong communication skills are essential to effectively engage with a diverse group of stakeholders, irrespective of their technical background. Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Requirements

Demonstrated track-record in modeling and experience utilizing model estimation tools such as Python or R
Ability to clearly communicate modeling results to management, model risk office, regulator and other modelers
Drive to continuously improve all aspects of their work in a collaborative fashion
Experience in machine learning
Strong communication skills with the ability to quickly understand existing models and new requirements/business needs
Experience working with Agile development methodologies
Strong grasp of econometric theory and methodologies
Desire to remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
Experience working with CCAR regulatory requirements
Experience with derivative modeling
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
At least 2 years of experience with Python, R or other statistical analyst software
At least 2 years of experience in statistical modeling or regression analytics or machine learning
At least 2 years of experience in derivative modeling (Fixed income, Commodity, FX or CDS)

Responsibilities

Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
Develop model approaches to assess model design and advance future capabilities
Understand relevant business processes and portfolios associated with model use
Understand technical issues in econometric, statistical, and machine learning modeling and apply these skills toward developing models and assessing model risks and opportunities
Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management
Maintain the efficiency and accuracy of our models through continuous improvement and application of best practices
Develop and maintain high quality and transparent documentation
Leverage the latest open source technologies and tools to identify areas of opportunity in our existing framework

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