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.
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