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Principal Quantitative Modeler
Company | Capital One |
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Location | McLean, VA, USA |
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Salary | $158600 – $181000 |
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Type | Full-Time |
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Degrees | |
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Experience Level | Senior, Expert or higher |
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Requirements
- 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.
Responsibilities
- 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.
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).