Senior Manager, Data Science - Cash Flow Underwriting

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

Senior Manager, Data Science for Cash Flow Underwriting at Capital One. This role focuses on leveraging advanced machine learning techniques to build and implement predictive models for risk and usage, transforming raw banking data into valuable features for forecasting creditworthiness and customer behavior. The role involves leading a team, strategic planning, and end-to-end model development.

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 7 years of experience performing data analytics OR Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 5 years of experience performing data analytics OR PhD in a quantitative field plus 2 years of experience performing data analytics
  • At least 2 years of experience leveraging open source programming languages for large scale data analysis
  • At least 2 years of experience working with machine learning
  • At least 2 years of experience utilizing relational databases

Nice to have

  • PhD in “STEM” field plus 4 years of experience in data analytics
  • At least 1 year of experience working with AWS
  • At least 1 year of experience managing people
  • At least 5 years’ experience in Python, Scala, or R for large scale data analysis
  • At least 5 years’ experience with machine learning

What the JD emphasized

  • advanced machine learning techniques
  • end-to-end model development
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Statistically-minded
  • built models, validated them, and backtested them

Other signals

  • leading the next wave of disruption
  • leveraging advanced machine learning techniques
  • identify and engineer the predictive signals
  • power our risk and usage models
  • uncover non-obvious patterns in cash flow and transaction data through sophisticated methodology
  • end-to-end model development
  • transforming raw banking data into high-value features
  • accurately forecast creditworthiness and customer behavior
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Statistically-minded
  • built models, validated them, and backtested them
  • experience with clustering, classification, sentiment analysis, time series, and deep learning