Applied Scientist Ii, Demand Science

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Applied Scientist II role focused on developing and deploying state-of-the-art ML/AI solutions for demand forecasting and supply optimization for Amazon Devices. The role involves full lifecycle ownership from research to production, including building forecasting models, exploring data, developing new approaches with AI-native experimentation and agent-driven automation, and partnering with engineering for deployment. Experience with transformer architectures, LLM-powered agents, and deep learning is required.

What you'd actually do

  1. Build forecasting models from prototype through production, working closely with engineering to deploy at scale
  2. Find and integrate new data sources to improve forecast accuracy and coverage
  3. Design and deliver production-ready solutions for business-critical forecasting and optimization problems
  4. Define and track performance metrics — both technical (error rates, bias, coverage) and business (plan attainment, financial impact, reduction in manual overrides)
  5. Write and maintain clear technical documentation; present findings and recommendations to scientists, engineers, and business leaders

Skills

Required

  • 4+ 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 programming in Java, C++, Python or related language
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience in building machine learning models for business application

Nice to have

  • Experience using Unix/Linux
  • PhD in computer science, machine learning, engineering, or related fields
  • Experience in any of the following areas: transformer model architectures (e.g., TFT, foundation models), neural network / deep learning model fine-tuning and development.

What the JD emphasized

  • state-of-the-art ML, AI, and automation solutions
  • transformer-based forecasting architectures
  • large language model (LLM)-powered agents
  • agentic AI workflows
  • AI-native experimentation
  • agent-driven automation
  • modern AI tools and frameworks
  • AI-driven automation
  • hands-off-the-wheel science operations
  • state-of-the-art deep learning models architecture design
  • deep learning training and optimization
  • model pruning
  • transformer model architectures
  • neural network / deep learning model fine-tuning and development

Other signals

  • ML/AI and communication skills
  • demand forecasting and supply optimization
  • transformer-based forecasting architectures
  • LLM-powered agents
  • agentic AI workflows
  • full lifecycle ownership (research through production deployment)
  • end-to-end solutions
  • AI-native experimentation
  • agent-driven automation
  • modern AI tools and frameworks
  • AI-driven automation
  • hands-off-the-wheel science operations
  • state-of-the-art deep learning models architecture design
  • deep learning training and optimization
  • model pruning
  • transformer model architectures
  • neural network / deep learning model fine-tuning and development