Applied Scientist Ii, Financial Insights and Actions

Amazon Amazon · Big Tech · CA, BC +1 · Research Science

Applied Scientist II role focused on leveraging GenAI/LLMs to build agentic solutions for financial insights and actions within Amazon. The role involves developing AI trust and safety in the financial domain, creating training/evaluation datasets for fine-tuning, and collaborating with engineers for productionalization. It balances scientific research with production deployment, with opportunities for external publications.

What you'd actually do

  1. Leveraging GenAI/LLMs to build agentic solutions to accelerate accounting-related research/tasks and produce proactive insights.
  2. Building AI trust and safety in the financial domain.
  3. Establishing scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation, and serving.
  4. Developing training/evaluation datasets for model fine-tuning.
  5. Collaborating with engineering to productionalize research.

Skills

Required

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • 3+ years of programming in Java, C++, Python or related language experience
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Experience applying theoretical models in an applied environment
  • Experience in professional software development

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • Leveraging GenAI/LLMs to build agentic solutions
  • Building AI trust and safety in the financial domain
  • Developing training/evaluation datasets for model fine-tuning
  • fine-tuning LLMs to provide recommendations