Senior Manager, Data Scientist - Applied AI

Capital One Capital One · Banking · McLean, VA +1

Senior Manager, Data Scientist - Applied AI role at Capital One, focusing on building and shipping Gen AI models for the US Card business. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch, AWS, Hugging Face, LangChain, and VectorDBs. Responsibilities include adapting and fine-tuning LLMs for customer-facing applications, building Gen AI and Sequence models through all development phases, and operationalizing them in production systems serving over 80 million customers. The ideal candidate is customer-focused, innovative, creative, a leader, technical with hands-on LLM experience, and influential. Experience in training language models, computer vision models, and subdomains like self-supervised learning, explainability, and RLHF is desired, along with an engineering mindset for delivering models at scale.

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, textual, and sequential transaction data.
  3. Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs) and Transformer-based architectures, adapting and fine-tuning them for customer-facing applications and features.
  4. Build Gen AI and Sequence models through all phases of development, from design and pre-training through 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—including model explainability and architectural trade-offs—into tangible business goals.

Skills

Required

  • quantitative field degree (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
  • experience performing data analytics
  • leveraging open source programming languages for large scale data analysis
  • working with machine learning
  • utilizing relational databases

Nice to have

  • PhD in STEM field
  • experience in data analytics
  • working with AWS
  • managing people
  • experience in Python, Scala, or R for large scale data analysis
  • experience working with LLMs
  • experience in training optimization
  • self-supervised learning
  • explainability
  • RLHF
  • engineering mindset
  • delivering models at scale
  • training data and inference volumes
  • delivering libraries, platforms, or solution level code

What the JD emphasized

  • state-of-the-art Gen AI models
  • massive scale
  • 80+ million customers
  • emerging technologies in Generative AI
  • world-class research and real-world production
  • global AI community
  • global payments ecosystem
  • AI-powered products
  • huge volumes of numeric, textual, and sequential transaction data
  • Large Language Models (LLMs)
  • Transformer-based architectures
  • customer-facing applications and features
  • Gen AI and Sequence models
  • scalable and resilient production systems
  • model explainability
  • architectural trade-offs
  • published state-of-the-art methods
  • hands-on experience working with LLMs
  • open-source tools and cloud computing platforms
  • AI/ML
  • breakthrough innovations
  • non-technical audiences
  • training language models
  • large computer vision models
  • explainability
  • RLHF
  • engineering mindset
  • delivering models at scale
  • training data and inference volumes
  • delivering libraries, platforms, or solution level code

Other signals

  • building and shipping state-of-the-art Gen AI models
  • delivering next-generation applications
  • delivering models at scale