Principal Associate, Data Scientist - Ms Clip Valuations

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

Capital One is seeking a Principal Associate, Data Scientist to build and manage models for their Mainstreet CLIP valuations team. This role involves designing and developing credit tools, collaborating with credit analysts, and driving business impact on a large lending program. The ideal candidate is customer-focused, creative, technical, and experienced with data analysis and machine learning.

What you'd actually do

  1. Design and develop a suite of sophisticated credit tools that will be used by a wide range of users, from credit analysts to data scientists. You'll leverage your Python skills to create solutions that are both powerful and user-friendly.
  2. Work closely with credit analysts to support and define our credit and lending strategy. This is a fantastic opportunity to learn about the intricacies of credit by building the very tools that enable critical decision-making.
  3. Your work will directly influence the P&L of our largest lending program, giving you a chance to see the tangible results of your efforts on a massive scale.
  4. Interact heavily with both credit analysts and data science partners, translating complex needs into actionable technical solutions.

Skills

Required

  • Bachelor's Degree in a quantitative field plus 5 years of experience performing data analytics
  • Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
  • PhD in a quantitative field
  • Python
  • SQL

Nice to have

  • Master’s Degree in “STEM” field plus 3 years of experience in data analytics
  • PhD in “STEM” field
  • AWS
  • Scala
  • R
  • machine learning

What the JD emphasized

  • machine learning
  • data analytics
  • Python
  • SQL

Other signals

  • models that power one of the largest exposure granting programs
  • extend $12+ billion in credit every year
  • directly influence the P&L of our largest lending program
  • machine learning technologies
  • data-driven decision-making