Applied Scientist, Sp Support Science

Amazon Amazon · Big Tech · Seattle, WA · Data Science

This role focuses on building and deploying LLM and ML systems for Amazon's Selling Partner Support. It involves creating NLP pipelines, multi-modal AI frameworks, and classification systems to identify and resolve customer support issues, processing millions of cases and informing business decisions.

What you'd actually do

  1. Use state-of-the-art Machine Learning and Generative AI techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed.
  2. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions.
  3. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features.
  4. Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model validation and model implementation.
  5. Research and implement novel machine learning and statistical approaches.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building machine learning models or developing algorithms for business application experience
  • Experience programming in Java, C++, Python or related language
  • 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 in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
  • Experience developing, deploying and managing AI products at scale
  • Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting

What the JD emphasized

  • building machine learning models or developing algorithms for business application experience
  • developing, deploying and managing AI products at scale

Other signals

  • LLM-powered classification
  • multi-modal friction detection
  • deploy production-grade NLP pipelines
  • multi-modal AI frameworks
  • scalable classification systems