Senior Associate, Data Scientist - Card Intelligence

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

Data Scientist role focused on building and deploying machine learning models for the credit card lifecycle (marketing, acquisitions, underwriting, fraud prevention) within a large financial institution. Leverages Python, AWS, Spark, and various ML techniques to drive business strategy and customer experiences.

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 (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 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

Nice to have

  • Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics), or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
  • Experience working with AWS
  • At least 2 years’ experience in Python, Scala, or R
  • At least 2 years’ experience with machine learning
  • At least 2 years’ experience with SQL

What the JD emphasized

  • machine learning models
  • machine learning technologies
  • machine learning

Other signals

  • build and deploy sophisticated data science and machine learning models
  • across the entire credit card lifecycle
  • turn complex insights into real-world impact
  • shaping financial products
  • machine learning technologies