Drimva

Capital One

Senior Associate, Data Scientist - Applied AI (Financial Services)

$123K–$155K

Plano, TX · Senior · Full-time · Posted Oct 2, 2026

Experience
Bachelor’s degree route requires 2 years of data analytics experience. Preferred experience: at least 2 years in Python, Scala, or R; machine learning; and SQL, and at least 1 year in AI, Information Retrieval, Deep Learning, or NLP.
Education
Bachelor’s degree in a quantitative field plus required experience, or a master’s degree in a quantitative field or an MBA with a quantitative concentration.
Visa sponsorship
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

About the job

Build and deploy Generative AI solutions, including multi-agent systems and advanced RAG pipelines, for financial services data and customer needs.

What you'll do

  • Partner with data scientists, machine learning engineers, and product managers to deliver Generative AI solutions.
  • Build multi-agent systems and RAG pipelines using numeric and textual data.
  • Develop AI models and pipelines through context engineering, fine-tuning, evaluation, validation, and deployment.

What they ask for

  • Bachelor’s degree in a quantitative field plus 2 years of data analytics experience, or a master’s degree in a quantitative field or an MBA with a quantitative concentration.
  • Hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
  • Experience building, validating, and backtesting models, including statistical and machine learning methods.

Nice to have

  • Master’s degree in a STEM field or PhD in a STEM field.
  • Experience with Python, Scala, or R; machine learning; SQL; or AI, Information Retrieval, Deep Learning, or NLP.

Pay

McLean, VA: $135,600 - $154,800 USD base salary; Plano, TX: $123,300 - $140,700 USD base salary

Tools

Python, PyTorch, LangChain, AWS, Spark, Vector Databases, Scala, R

AI focus

Build Generative AI solutions, multi-agent systems, and RAG pipelines; handle context engineering, fine-tuning, evaluation, validation, and deployment.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)