Manager, Data Scientist - Model Risk Office

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

This role is within Capital One's Model Risk Office, focusing on identifying and quantifying risks associated with models, including Generative AI. The Data Scientist will build machine learning models to challenge existing production models and contribute to the model governance framework. They will validate models across various business domains and present findings to executives. The role requires experience with ML, data analysis, and databases, with a preference for GenAI and model validation experience.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
  2. Leverage a broad stack of technologies — from foundational frameworks (PyTorch, Hugging Face), to orchestration tools (LangChain, Vector Databases) to LLMOps, observability platforms, and more — to reveal the insights hidden within huge volumes of multi-modal data
  3. Build machine learning models to challenge “champion models” that are deployed in production today and contribute to the model governance framework for the next generation of models
  4. Validate a wide variety of models across multiple business domains within our Enterprise Services division, and flex your interpersonal skills to present how identified model risks could impact the business to executives.

Skills

Required

  • Bachelor's Degree in a quantitative field or equivalent experience
  • 6 years of experience performing data analytics (or Master's/4 years, or PhD/1 year)
  • 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 or vector 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, including GenAI
  • 4 years’ experience building or validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection.

What the JD emphasized

  • model governance framework
  • validate a wide variety of models
  • quantify risks associated with models

Other signals

  • Model Risk Management
  • Generative AI Risks
  • Challenging Production Models
  • Model Governance