Principal, Data Scientist - Card Intelligence

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

This role focuses on building and deploying machine learning models for the credit card lifecycle, including marketing, underwriting, and fraud prevention. The candidate will work with a cross-functional team, leverage technologies like Python and AWS, and be responsible for all phases of model development from design to implementation. The role requires a strong statistical background and experience with various machine learning techniques.

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
  • Master's Degree in a quantitative field or MBA with a quantitative concentration
  • PhD in a quantitative field
  • Experience performing data analytics
  • Experience with Python, Scala, or R
  • Experience with SQL
  • Experience with machine learning
  • Experience with AWS

Nice to have

  • Master’s Degree in STEM field
  • PhD in STEM field

What the JD emphasized

  • Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
  • At least 1 year of experience working with AWS
  • At least 3 years’ experience in Python, Scala, or R
  • At least 3 years’ experience with machine learning
  • At least 3 years’ experience with SQL

Other signals

  • builds and deploys sophisticated data science and machine learning models
  • across the entire credit card lifecycle—including marketing, acquisitions, underwriting, and fraud prevention
  • turn complex insights into real-world impact
  • build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation