Manager, Data Science - AI Foundations

Capital One Capital One · Banking · New York, NY +2

Manager, Data Science - AI Foundations role at Capital One focuses on building and shipping AI/ML solutions for customer-facing applications, including personalization and digital assistants. The role involves leveraging LLMs, fine-tuning them, and building ML/NLP models through all development phases, from design to production operationalization. It emphasizes partnering with cross-functional teams and utilizing technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
  2. Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
  3. Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
  4. Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
  5. Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Skills

Required

  • Data Science
  • AI/ML
  • NLP
  • LLMs
  • PyTorch
  • AWS
  • Hugging Face
  • LangChain
  • VectorDBs
  • Python
  • SQL
  • Machine Learning
  • Data Analytics

Nice to have

  • PhD in STEM
  • Scala
  • R
  • Computer Vision
  • Training Optimization
  • Self-supervised learning
  • Explainability
  • RLHF

What the JD emphasized

  • building and shipping state of the art scalable architecture, AI/ML solutions
  • operationalize them in scalable and resilient production systems
  • delivering models at scale both in training data and inference volumes
  • experienced in training language models or large computer vision models
  • expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF

Other signals

  • building and shipping state of the art scalable architecture, AI/ML solutions
  • delivering models at scale both in training data and inference volumes
  • operationalize them in scalable and resilient production systems