Principal Associate, Data Scientist - Bank Operations Data Science

Capital One Capital One · Banking · McLean, VA

The Bank Operations Data Science team builds machine learning models for internal capabilities like Check/Document Reading, Anomaly Identification, Natural Language Processing of Calls, and operational forecasting, using technologies like neural networks, LLMs, and transformer architectures. The role involves partnering with cross-functional teams, leveraging technologies like Python and Snowflake, and building ML models through all phases of development. The ideal candidate is innovative, creative, technical, and statistically-minded, with experience in various ML techniques and cloud platforms. Specific experience with LLM risk mitigation is preferred.

What you'd actually do

  1. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  2. Leverage a broad stack of technologies — Python, Snowflake and more — to reveal the insights hidden within huge volumes of numeric and textual data
  3. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  4. This role works directly with our call center operations team to streamline call processing including summarization and automation to help our call center associates better serve our customers.
  5. 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 5 years of experience performing data analytics OR Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 3 years of experience performing data analytics OR PhD in a quantitative field
  • Python
  • Snowflake
  • SQL
  • machine learning

Nice to have

  • Master’s Degree in “STEM” field plus 3 years of experience in data analytics, or PhD in “STEM” field
  • AWS
  • Scala
  • R
  • Proven track record of identifying and mitigating risks specifically linked to LLMs
  • Proven ability to create and maintain technical documentation

What the JD emphasized

  • Proven track record of identifying and mitigating risks specifically linked to LLMs

Other signals

  • build machine learning models
  • transformer architectures
  • LLMs
  • neural networks