Principal Associate, Data Scientist - Card Finance Innovation Team

Capital One Capital One · Banking · McLean, VA

Data Scientist role focused on building machine learning models for financial forecasting and innovation within Capital One's Card Finance team. The role involves leveraging a broad tech stack including Python, AWS, H2O, and Spark to develop models through all phases of development, from design to implementation, with an emphasis on statistical modeling and deep 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 plus 5 years of experience performing data analytics
  • Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
  • PhD in a quantitative field
  • Python
  • AWS
  • SQL
  • clustering
  • classification
  • sentiment analysis
  • time series
  • deep learning

Nice to have

  • Master’s Degree in “STEM” field plus 3 years of experience in data analytics, or PhD in “STEM” field
  • Scala
  • R
  • 1 year of experience working with AWS

What the JD emphasized

  • Machine learning
  • data analytics
  • Python
  • AWS
  • SQL
  • clustering
  • classification
  • sentiment analysis
  • time series
  • deep learning

Other signals

  • build machine learning models through all phases of development
  • design through training, evaluation, validation, and implementation
  • experience with clustering, classification, sentiment analysis, time series, and deep learning