Manager, Data Scientist - Model Risk Office

Capital One Capital One · Banking · McLean, VA +1

Manager of Data Scientists within the Model Risk Office, focusing on validating and building statistical or machine learning models, including those with unique risks associated with Generative AI. The role involves partnering with cross-functional teams, leveraging technologies like Python and AWS, and translating complex work into business goals. The ideal candidate is creative, a leader, statistically-minded, and a data guru.

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. Validate and build statistical or 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, financial modeling or financial model validation
  • 1 year of experience working with AWS
  • 4 years’ experience in Python, Scala, or R for large scale data analysis
  • 4 years’ experience with statistical modeling or machine learning
  • 4 years’ experience with SQL

What the JD emphasized

  • unique risks associated with Generative AI
  • model failures
  • model validation
  • statistical modeling
  • machine learning models

Other signals

  • Model Risk Management
  • Generative AI risks
  • Model validation
  • Statistical modeling
  • Machine learning