Senior Manager, Data Science - Model Risk Office

Capital One Capital One · Banking · McLean, VA +3

Senior Manager, Data Science for Capital One's Model Risk Office, focusing on evaluating risk for Card Fraud models. The role involves building challenger models using advanced algorithms and providing mentorship. This is a management role within a fintech domain, focused on the application and risk assessment of AI/ML models in a production environment.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  2. Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
  3. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  4. Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Required

  • Bachelor's Degree in a quantitative field or equivalent experience
  • 7 years of experience performing data analytics
  • 2 years of experience leveraging open source programming languages for large scale data analysis
  • 2 years of experience working with machine learning
  • 2 years of experience utilizing relational databases

Nice to have

  • PhD in STEM field
  • 4 years of experience in data analytics
  • 1 year of experience working with AWS
  • 1 year of experience managing people
  • 5 years’ experience in Python, Scala, or R for large scale data analysis
  • 5 years’ experience with hands-on Machine Learning model development and deployment
  • Experience with model development/deployment pipeline (e.g. Kubeflow, Kubernetes)

What the JD emphasized

  • Model Risk Office
  • Card Fraud models
  • evaluating risk
  • challenger models
  • model risk

Other signals

  • evaluating risk for Card Fraud models
  • builds independent challenger models
  • investigating advanced algorithms such as graph, neural, tree-based, and sequence methods to create robust challenger models
  • oversee card fraud model risk