Senior Associate, Data Scientist - Card Dfs Integration

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

This role focuses on building and implementing machine learning models for the Discover Card Customer Management Data Science team, aiming to manage and improve underwriting models. It involves leveraging technologies like Python, AWS, and Spark to analyze data and develop models through their full lifecycle, including training and validation, within the fintech domain.

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

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
  • training, evaluation, validation, and implementation
  • clustering, classification, sentiment analysis, time series, and deep learning
  • analyze data

Other signals

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