AI Foundations Data Science Lead

Capital One; McLean (VA); Presencial; USD 193 000 - 221 000; Tempo integral

Resumo da oferta

Capital One is seeking a Manager, Data Science - AI Foundations to lead AI-powered products that transform how customers interact with their money. You will collaborate with data scientists, software engineers, and product managers to design, train, and deploy NLP and ML models at scale across billions of records.

You will leverage PyTorch, AWS, Hugging Face, LangChain, and vector databases to extract insights, fine-tune LLMs for customer-facing features, and translate technical results into

Qualificações

• Bachelor’s or higher in a quantitative field with data analytics experience
• Graduate degrees with data analytics focus count towards qualifications
• Experience with open source programming languages for large-scale data analytics
• Experience with machine learning and relational databases

Responsabilidades

• Partner with cross-functional teams to deliver AI-powered products
• Utilize PyTorch, AWS Ultraclusters, Hugging Face, LangChain, and VectorDBs to analyze large data
• Develop and fine-tune NLP models for customer-facing applications
• Build ML/NLP models through design, training, evaluation, and deployment
• Communicate complex work in business terms to stakeholders

Conhecimentos

Python

SQL

Relational Databases

Machine Learning

Formação académica

Bachelor's degree in a quantitative field

Master’s degree in a quantitative field or MBA with quantitative concentration

PhD in a quantitative field

Ferramentas

PyTorch

AWS Ultraclusters

Hugging Face

LangChain

Lightning

VectorDBs

Descrição da oferta de emprego

Capital One is seeking a Manager, Data Science - AI Foundations to lead AI-powered products that transform how customers interact with their money. You will collaborate with data scientists, software engineers, and product managers to design, train, and deploy NLP and ML models at scale across billions of records.

You will leverage PyTorch, AWS, Hugging Face, LangChain, and vector databases to extract insights, fine-tune LLMs for customer-facing features, and translate technical results into