Capital One
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Manager,DataScience-ModelRiskOffice
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“Manager, Data Science - Model Risk Office at Capital One. Skills: Model validation, Machine learning, Statistical modeling, Data analysis. Perform independent model validations. Validate fraud and AML modeling approaches”
Industry & Context.
Data-driven decision-making; Applying skills toward developing models; Assessing model risks and opportunities; Creative; Asking questions; Pushing hard to find answers
What They're Looking For.
Must Have
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, 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, 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, 1 year of experience leveraging open source programming languages for large scale data analysis, 1 year of experience working with machine learning, 1 year of experience utilizing relational databases
Nice to Have
PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, 1 year of experience working with AWS, 4 years’ experience in Python, Scala, or R for large scale data analysis, 4 years’ experience with machine learning
What You'll Do.
Perform independent model validations
Validate fraud and AML modeling approaches
Remain on the leading edge of analytical technology
Understand relevant business processes
Understand technical issues
Communicate technical subject matter
Maintain the efficiency and accuracy of models
Develop and maintain documentation
Leverage open-source technologies
How You'll Work.
Team & Collaboration
Work closely with business groups; Engage with diverse stakeholders; Communicate to non-specialist audiences; Present to senior management
Communication Scope
Communication skills are essential; Effectively engage with stakeholders; Communicate technical subject matter clearly; Communicate concisely; Prepare presentations of complex technical concepts
Full Job Description
Manager, Data Science - Model Risk Office 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. **Role Description** **In this role, you will:** * 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, statistica
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