Capital One's Credit Risk Management Modeling team is looking to develop econometric and machine learning models and analytics to predict and generate insight into Capital One's risk and capital needs. These models will inform critical business decisions related to loss forecasting, CECL/allowance, stress testing, and capital allocation, ultimately impacting earnings call preparation and downturn preparedness initiatives.
Requirements
- Experience utilizing model estimation tools.
- 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.)
Responsibilities
- Develop econometric and machine learning models and analytics to predict and generate insight into Capital One’s risk and capital needs.
- 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.
- 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.
- Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights and decision frameworks for our business and customers.
Other
- Providing technical guidance to business leadership.
- Communicate technical subject matter clearly and concisely to individuals from various backgrounds.
- Strong understanding of quantitative analysis methods in relation to financial institutions.
- Demonstrated track-record in machine learning and/or econometric analysis.
- 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.
