Manager, Data Scientist - Card Intelligence Model Risk Management

Capital One Capital One · Banking · McLean, VA +2

Manager, Data Scientist role focused on Card Intelligence Model Risk Management within a fintech company. The role involves building machine learning models through all phases of development, from design through training, evaluation, validation, and implementation, and partnering with cross-functional teams. It emphasizes statistical modeling, data analysis, and applying emerging technologies to financial products.

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
  • 6 years 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
  • 3 years of experience in data analytics
  • 1 year of experience working with AWS
  • 3 years’ experience in leading model governance and end to end model development
  • 4 years’ experience in Python, Scala, or R for large scale data analysis
  • 4 years’ experience with machine learning
  • 4 years’ experience with SQL

What the JD emphasized

  • model risk management
  • model development methodology standards
  • model validation
  • end to end model development

Other signals

  • model risk management
  • model development
  • machine learning implementation
  • data-driven decision-making