Data Scientist, Prime Video Science

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

This role focuses on evaluating AI systems, building benchmarks, and analyzing model outputs within the Prime Video Science team. It involves designing experiments, applying statistical and causal inference methods, and communicating findings to both technical and business stakeholders. The role is crucial for ensuring the quality and impact of AI systems used in content decisions and business investments.

What you'd actually do

  1. Design and analyze randomized experiments that validate and calibrate our models and measure the impact of content and product changes.
  2. Build evaluations and benchmarks for the AI systems the team develops and define what "good" looks like for them.
  3. Run deep-dive analyses on model outputs, experiment results, and customer behavior to surface the story behind the numbers and catch issues before they reach stakeholders.
  4. Apply statistical modeling, causal inference, and data analysis to answer business questions and inform major investment decisions.
  5. Communicate findings to business, finance, engineering, and science stakeholders through clear written analyses and business-facing documents.

Skills

Required

  • Master's degree or above in a quantitative field
  • 2+ years of data scientist experience
  • 2+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 2+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
  • 1+ years of working with or evaluating AI systems experience

Nice to have

  • Ph.D. in a quantitative field
  • Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
  • Experience in a ML or data scientist role with a large technology company
  • Experience in defining and creating benchmarks for assessing GenAI model performance
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • evaluating AI systems
  • build evaluations and benchmarks for AI systems
  • run deep-dive analyses on model outputs
  • applying statistical modeling, causal inference, and data analysis
  • turning research into innovations

Other signals

  • evaluating AI systems
  • build evaluations and benchmarks for AI systems
  • run deep-dive analyses on model outputs
  • applying statistical modeling, causal inference, and data analysis
  • turning research into innovations