Capital One

Position Title/Short Description

Full Text of JOE Listing:

As a Quantitative Modeler 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 is part of Capital One’s Credit Risk Management Modeling team. In this team, we use multiple cloud-based open-sourced technologies to develop econometric and machine learning models and analytics to predict and generate insight into Capital One’s risk and capital needs. We blend cutting-edge quantitative methods with deep understanding of our business, data, and regulatory environment to build predictive models for credit and operational losses, volumes and outstanding balances in support of loss forecasting, CECL/allowance, stress testing, and capital allocation for Capital One. Our models and analyses inform earnings call prep, vertical resilience and downturn preparedness initiatives.

In this position, you’ll be part of a team tasked with ushering in the next wave of disruption to predictive modeling - using technology to build & deploy models and solutions leveraging new data sources to provide powerful new insights about our portfolio resilience and opportunities.

Responsibilities and Skills:
- Partner with the various lines of business to enhance modeling and analytical framework.
- 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 or machine learning to improve business performance.
- Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights and decision frameworks for our business and customers.
- Providing technical guidance to business leadership.
- Communicate technical subject matter clearly and concisely to individuals from various backgrounds.

Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Successful candidates would possess:
- Strong understanding of quantitative analysis methods in relation to financial institutions.
- Demonstrated track-record in machine learning and/or econometric analysis.
- 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.
- Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.

Basic Qualifications:
- Bachelor’s Degree plus at least 5 years of experience in data analytics, or Master’s Degree plus at least 3 years in data analytics, financial modeling or econometric modeling (can include Graduate School Research work) or PhD.
- At least 2 years of programming experience.

Preferred Qualifications:
- PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related disciplines.
- 1 year of experience with Python, R or other statistical analyst software.
- 2 years of experience with data analysis.
- 1 year of experience manipulating and analyzing large data sets.
- Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, decision trees, machine learning methods.)

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.