Manager, Data Science - Model Risk Office

Capital One Capital One · Banking · McLean, VA +1

Manager for a Model Validation team within the Model Risk Office, responsible for validating payment network business models (fraud, AML, counterparty risk, financial models) in accordance with regulatory guidance and internal policies. The role involves assessing statistical and machine learning models, communicating technical concepts to diverse stakeholders, and leveraging open-source technologies for continuous improvement.

What you'd actually do

  1. 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.
  2. Validate fraud and AML modeling approaches, including: - Rule-based systems and thresholds - Statistical models - Machine learning models, (e.g., Gradient Boosting, Random Forecast)
  3. Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
  4. Understand relevant business processes and portfolios associated with model use
  5. Understand technical issues in econometric, statistical, and machine learning modeling and apply these skills toward developing models and assessing model risks and opportunities

Skills

Required

  • Bachelor's Degree in a quantitative field plus 6 years of experience performing data analytics OR Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics OR PhD in a quantitative field plus 1 year of experience performing data analytics
  • Leveraging open source programming languages for large scale data analysis
  • Working with machine learning
  • Utilizing relational databases

Nice to have

  • PhD in STEM field plus 3 years of experience in data analytics
  • AWS
  • Python, Scala, or R for large scale data analysis
  • machine learning

What the JD emphasized

  • regulatory guidanceSR 11-7
  • internal model risk policy and standards
  • fraud
  • AML
  • counterparty risk
  • financial models

Other signals

  • Validating payment network business models
  • Validating fraud and AML modeling approaches
  • Machine learning models, (e.g., Gradient Boosting, Random Forecast)