Principal Associate, Quantitative Analyst - Quantitative Finance
Capital OneAbout the role
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.
This position offers a unique opportunity to be a part of the dynamic Finance organization at Capital One. The Finance organization has been on a technology journey seeking to find ways to leverage technology to drive deeper insights and make the complex simple. We are looking for candidates to help in our journey with modeling, analytical and/or model implementation skills to join our finance team.
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 will possess:
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
Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring
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)
Basic Qualifications:
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 5 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 3 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)
Preferred Qualifications:
Master’s Degree or PhD in Statistics, Economics, Mathematics, Financial Engineering, Operations Research, Engineering, Finance, Physics or related discipline
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