Manager, Data Scientist - Financial Services

Capital One Capital One · Banking · Plano, TX

Capital One is seeking a Manager, Data Scientist for their Auto Finance business. This role involves building machine learning models for applications like customer lifetime valuation, product recommendation, fraud detection, and productivity improvement via Generative AI. The candidate will work with a cross-functional team, leverage technologies like Python, AWS, and Spark, and translate complex work into business goals. Requires a strong quantitative background, experience with data analysis, machine learning, and relational databases, with a preference for a PhD and AWS experience.

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 6 years of experience performing data analytics OR Master's Degree in a quantitative field plus 4 years of experience performing data analytics OR PhD in a quantitative field plus 1 year of experience performing data analytics
  • At least 1 year of experience leveraging open source programming languages for large scale data analysis
  • At least 1 year of experience working with machine learning
  • At least 1 year of experience utilizing relational databases

Nice to have

  • PhD in “STEM” field plus 3 years of experience in data analytics
  • At least 1 year of experience working with AWS
  • At least 4 years’ experience in Python, Scala, or R for large scale data analysis
  • At least 4 years’ experience with machine learning
  • At least 4 years’ experience with SQL

What the JD emphasized

  • Machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

Other signals

  • customer lifetime valuation
  • product recommendation
  • fraud detection
  • Generative AI