Principal Associate, Data Scientist - Anti-money Laundering

Capital One Capital One · Banking · McLean, VA +3

This role focuses on building and deploying AI/ML models for Anti-Money Laundering (AML) within a financial services context. The responsibilities include developing production-ready pipelines, building ML models and AI tools, and specifically fine-tuning, evaluating, and productionizing LLMs. The role involves working with technologies like Python, AWS, Spark, and LLM-specific tools such as LangGraph and LlamaIndex, with a strong emphasis on delivering industry-leading risk management products.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, business analysts, risk managers, and product owners to deliver industry-leading risk management products
  2. Leverage a broad stack of tools and technologies — Python, Conda, AWS, Spark, dbt, and more — to build production-ready pipelines for data sourcing, model development, and model scoring
  3. Build machine learning models and AI tools through all phases of development, from design through training, evaluation, validation, and implementation
  4. Fine tune, evaluate, customize, and productionize Large Language Models (LLMs)
  5. Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Required

  • Python
  • SQL
  • AWS
  • machine learning
  • data analytics
  • quantitative field degree

Nice to have

  • AML modeling
  • GenAI
  • Agentic AI
  • LLMs
  • vector databases
  • LLM fine tuning
  • RAG
  • LangGraph
  • LlamaIndex

What the JD emphasized

  • production-grade GenAI, Agentic AI, and/or LLMs based systems
  • LLM fine tuning
  • RAG
  • LangGraph or LlamaIndex

Other signals

  • Develops and evaluates production-grade GenAI, Agentic AI, and/or LLMs based systems
  • Fine tune, evaluate, customize, and productionize Large Language Models (LLMs)
  • Build machine learning models and AI tools through all phases of development