Manager, Data Scientist - Model Risk Office

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

Manager, Data Scientist in Capital One's Model Risk Office. This role focuses on defending the company against model failures and improving decision-making through models. Responsibilities include partnering with cross-functional teams, leveraging technologies like Python and AWS, and building machine learning models through all development phases (design, training, evaluation, validation, implementation). The ideal candidate is innovative, creative, technical, and statistically-minded with experience in various ML techniques and cloud platforms. The role requires significant experience in data analytics and machine learning, 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 or equivalent experience
  • 6 years of experience performing data analytics
  • 1 year of experience leveraging open source programming languages for large scale data analysis
  • 1 year of experience working with machine learning
  • 1 year of experience utilizing relational databases

Nice to have

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

What the JD emphasized

  • Model Risk Office
  • defend the company against model failures
  • Enterprise Risk Management
  • build machine learning models through all phases of development
  • published state-of-the-art methods

Other signals

  • Model Risk Office
  • defend the company against model failures
  • Enterprise Risk Management
  • build machine learning models through all phases of development