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. As part of Capital One's Model Audit team, you will have a unique vantage point to review models and model risk practices across the enterprise and the opportunity to connect the dots to raise the appropriate model risk issues and provide assurance to the Audit Committee. Successful candidates will partner cross-functionally with business throughout the company to deliver breakthrough analytical solutions to support a winning strategy in a continually changing business environment.
Responsibilities
- Apply deep expertise in econometric, statistical and machine learning methods to generate critical insights and decision frameworks for our business and customers.
- Communicate technical subject matter clearly and concisely to individuals from various backgrounds.
- 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, machine learning, and automation solutions to improve business performance and process efficiencies.
- Collaborate in a cross-disciplinary team to build cloud-based solutions grounded in data.
Requirements
- Strong understanding of quantitative analysis methods in relation to financial institutions.
- Demonstrated track-record in machine learning and 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.
- 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).
Nice-to-haves
- PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline.
- 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.
Benefits
- Comprehensive health benefits
- Financial benefits
- Inclusive set of benefits supporting total well-being
