Applied Scientist Ii, Amazon Private Brands

Amazon Amazon · Big Tech · CA, BC +1 · Machine Learning Science

Applied Scientist II role focused on building and deploying Generative AI and Machine Learning solutions for Amazon Private Brands' product assortment, pricing, and merchandising. The role involves adapting state-of-the-art research, inventing novel solutions, developing prototypes, and implementing production software, with a focus on agentic AI and large language models. Responsibilities include partnering with business stakeholders, adapting ML solutions, inventing and deploying novel science solutions, reviewing team's work, and publishing research.

What you'd actually do

  1. Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions.
  2. Adapt and apply state-of-the-art machine learning solutions to business problems.
  3. Invent novel science solutions, develop prototypes, and deploy production software to solve business problems.
  4. Review and guide science solutions across the team.
  5. Publish and socialize your and the team's research across Amazon and external avenues as appropriate

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • 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

  • PhD in computer science, machine learning, engineering, or related fields
  • Experience building machine learning models or developing algorithms for business application
  • Experience in professional software development
  • Experience applying theoretical models in an applied environment
  • Experience using Unix/Linux
  • Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
  • Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

What the JD emphasized

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

Other signals

  • Generative AI
  • Machine Learning
  • Statistics
  • Economics solutions
  • product assortment
  • strategic business decisions
  • product inputs
  • title, price, merchandising and ordering
  • Scientists, Economists, Engineers, and Product Managers
  • incubating and building day one solutions
  • novel technology
  • business problems
  • named entity recognition
  • product substitutes
  • pricing optimization
  • agentic AI
  • large language models
  • raise the team bar for science research and development
  • high visibility opportunity
  • ambitious science solutions
  • direct business and customer impact
  • state-of-the-art machine learning solutions
  • invent novel science solutions
  • develop prototypes
  • deploy production software
  • review and guide science solutions
  • publish and socialize research
  • applied science practices, principles & processes
  • building models for business application
  • patents or publications
  • top-tier peer-reviewed conferences or journals
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing
  • building machine learning models
  • developing algorithms for business application
  • professional software development
  • applying theoretical models in an applied environment
  • generative AI tools
  • workflow efficiency
  • effective prompting
  • evaluation practices
  • recognize opportunities where generative AI could enhance products, workflows, or customer experiences