Manager, Data Science - AI Software Engineering

Capital One Capital One · Banking · McLean, VA +2

Manager of Data Science focused on AI Software Engineering, designing and building scalable AI architectures for the software development lifecycle using multi-agent solutions. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building ML models through all phases of development. Experience with agentic platforms, RAG, and advanced model customization is preferred.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  2. Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
  3. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  4. Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Required

  • Bachelor's Degree in a quantitative field or equivalent experience
  • Master's Degree in a quantitative field or MBA with quantitative concentration or equivalent experience
  • PhD in a quantitative field or equivalent experience
  • open source programming languages for large scale data analysis
  • machine learning
  • relational databases

Nice to have

  • PhD in STEM field
  • AWS
  • production-grade agentic platforms
  • RAG
  • graph-augmented systems
  • MCP
  • tool-calling integrations
  • advanced model customization techniques
  • fine-tuning
  • parameter-efficient tuning (LoRA/QLoRA)
  • reinforcement learning
  • preference optimization
  • Prior research and publications in AI/ML conferences

What the JD emphasized

  • multi-agent solutions
  • advanced model customization techniques
  • production-grade agentic platforms
  • RAG
  • graph-augmented systems
  • tool-calling integrations
  • fine-tuning
  • parameter-efficient tuning (LoRA/QLoRA)
  • reinforcement learning
  • preference optimization

Other signals

  • designing, building, and delivering state-of-the-art, scalable AI architectures
  • multi-agent solutions across the software development lifecycle
  • advanced model customization techniques