Senior AI/Data Scientist – GenAI for Customer Experience

Capital One; McLean (VA); On-site; USD 136,000 - 155,000; Full time

Job summary

Capital One is seeking a data scientist to advance AI across unstructured data, including text and images, using open-source LLMs and visual language models. You will work with LangGraph, LlamaIndex, Weave, Hugging Face, PyTorch, and AWS to build end-to-end ML/NLP solutions for a high-volume customer environment.

You will collaborate with product, tech, and business teams to operationalize models, ensure observability, and guide annotators in creating high-quality datasets for training and

Qualifications

• Bachelor's or Master's degree in a quantitative field or MBA with quantitative focus.
• 2+ years of data analytics experience preferred.
• Strong python/scala/R experience and ML model development.

Responsibilities

• Apply unstructured data (text, image) with LLMs and VLMs to build production-ready models.
• Develop ML/NLP solutions across design, training, evaluation, and deployment.
• Collaborate with engineers to operationalize models in scalable systems serving millions.
• Assess GenAI/LLM architectures and ensure AI observability and governance.
• Guide data annotators and curate high-quality datasets for training and monitoring.
• Partner with product and business teams to align DS solutions with outcomes.

Skills

Python

Scala

R

Machine learning

SQL

AWS

Open-source tools

Education

Bachelor's Degree in a quantitative field

Master's Degree in a quantitative field or MBA with quantitative concentration

Tools

LangGraph

LlamaIndex

Weights & Biases

Weave

Hugging Face

PyTorch

AWS

Job description

Capital One is seeking a data scientist to advance AI across unstructured data, including text and images, using open-source LLMs and visual language models. You will work with LangGraph, LlamaIndex, Weave, Hugging Face, PyTorch, and AWS to build end-to-end ML/NLP solutions for a high-volume customer environment.

You will collaborate with product, tech, and business teams to operationalize models, ensure observability, and guide annotators in creating high-quality datasets for training and