Applied Scientist, Prime Video Science

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

Applied Scientist role at Amazon Prime Video focusing on building ML and deep learning models to simulate customer behavior and guide content/product investment decisions. The role involves applying techniques like reinforcement learning, causal inference, and designing agentic AI systems, with a strong emphasis on translating research into production-ready systems and communicating insights to stakeholders.

What you'd actually do

  1. Build models that simulate customer behavior to answer counterfactual "what-if" questions that guide major content and product investment decisions.
  2. Apply deep learning, reinforcement learning, causal inference, and experimental design to large-scale customer data to model how customers respond to change.
  3. Design and prototype agentic AI systems and other novel ML approaches and research new methods to improve the accuracy and scale of our models.
  4. Validate and calibrate models against real-world randomized experiments, and partner with software engineers to deliver scalable, production-ready systems.
  5. Translate model outputs into clear recommendations and communicate results to business, finance, and science stakeholders through both technical papers and business-facing documents.

Skills

Required

  • building machine learning models
  • developing algorithms for business application
  • 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
  • programming in Java, C++, Python or related language

Nice to have

  • state-of-the-art deep learning models architecture design
  • deep learning training and optimization
  • model pruning
  • investigating, designing, prototyping, and delivering new and innovative system solutions
  • professional software development
  • reinforcement learning
  • agentic AI system design
  • causal inference

What the JD emphasized

  • build machine learning models
  • reinforcement learning
  • causal inference
  • agentic AI systems
  • novel ML approaches
  • production-ready systems
  • publications at top-tier peer-reviewed conferences or journals

Other signals

  • builds ML models
  • simulates customer behavior
  • agentic AI systems
  • novel ML approaches