Principal Associate, Data Scientist - Partnerships Acquisitions

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

Capital One is seeking a Principal Associate Data Scientist to join their Partnerships Acquisitions team. This role focuses on building and implementing machine learning models for credit decisioning, product optimization, and customer valuation within the fintech domain. The position requires a strong background in data science, machine learning, and statistical modeling, with experience in Python, AWS, and SQL. The candidate will work with a cross-functional team to deliver data-driven products and enhance decision accuracy and efficiency.

What you'd actually do

  1. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  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. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  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 5 years of experience performing data science
  • 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 plus 3 years of experience performing data analytics
  • PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
  • Python
  • SQL
  • machine learning
  • statistical modeling

Nice to have

  • 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)
  • AWS
  • Scala
  • R
  • clustering
  • classification
  • sentiment analysis
  • time series
  • deep learning

What the JD emphasized

  • machine learning models
  • credit decisioning
  • alternative data sources
  • advanced modeling techniques
  • customer records
  • machine learning technologies
  • data-driven decision-making
  • machine learning models
  • credit decisioning
  • product optimization
  • customer valuation
  • machine learning models
  • design through training, evaluation, validation, and implementation
  • huge volumes of numeric and textual data
  • analyzing and creating
  • published state-of-the-art methods, technologies, and applications
  • data science solutions
  • cloud computing platforms
  • built models, validated them, and backtested them
  • clustering, classification, sentiment analysis, time series, and deep learning

Other signals

  • building the next generation of machine learning models
  • credit decisioning
  • alternative data sources
  • advanced modeling techniques