Manager, Data Science - Model Risk Office

Capital One•McLean, VA

19d

About The Position

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 Data Scientist 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 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. Capital One is selectively recruiting for a Manager for a Model Validation team. The individual would report to the Model Risk Office and work closely with the business groups. This position is responsible for validating payment network business models, including fraud risk, Anti-Money Laundering (AML), Counterparty risk, and financial models. Strong communication skills are essential to effectively engage with a diverse group of stakeholders, irrespective of their technical background.

Requirements

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 6 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 4 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) plus 1 year of experience performing data analytics
At least 1 year of experience leveraging open source programming languages for large scale data analysis
At least 1 year of experience working with machine learning
At least 1 year of experience utilizing relational databases

Nice To Haves

PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
At least 1 year of experience working with AWS
At least 4 years’ experience in Python, Scala, or R for large scale data analysis
At least 4 years’ experience with machine learning

Responsibilities

Perform independent model validations for payment network models, including fraud, AML, counterparty and financial models in accordance with regulatory guidanceSR 11-7 and internal model risk policy and standards.
Validate fraud and AML modeling approaches, including: Rule-based systems and thresholds, Statistical models, Machine learning models, (e.g., Gradient Boosting, Random Forecast)
Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
Understand relevant business processes and portfolios associated with model use
Understand technical issues in econometric, statistical, and machine learning modeling and apply these skills toward developing models and assessing model risks and opportunities
Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management
Maintain the efficiency and accuracy of our models through continuous improvement and application of best practices
Develop and maintain high quality and transparent documentation
Leverage the latest open-source technologies and tools to identify areas of opportunity in our existing framework