Economist, Whs Central Science

Amazon Amazon · Big Tech · Bellevue, WA · Economics

This role focuses on applying economic expertise, causal inference, and machine learning to research and model factors driving workplace safety outcomes within Amazon's operations. The goal is to identify effective improvement programs and develop data-driven solutions to reduce risks and injuries, partnering with business and technical teams to deliver impactful insights and scalable data resources.

What you'd actually do

  1. Work with engineers and data scientists on large-scale data modeling
  2. Combine strong economic expertise with interdisciplinary learning
  3. Execute big ideas as part of a technical team
  4. Apply expertise in causal modeling and machine learning to measure the impact of key initiatives on safety outcomes.
  5. Develop and maintain attribution models to understand the key drivers of safety performance.

Skills

Required

  • PhD in economics or equivalent
  • causal inference
  • impact estimation
  • large-scale data modeling
  • machine learning
  • attribution models
  • experimental design

Nice to have

  • 2+ years of industry, consulting, government, or academic research experience
  • Knowledge of at least one statistical software package such as R, Stata, Matlab, SAS
  • Experience in prediction and forecasting in a research or industrial environment
  • Experience with handling of large datasets

What the JD emphasized

  • partner effectively with both business and technical teams
  • clear communication of results
  • influence a variety of stakeholders
  • experience applying causal inference techniques
  • impact estimation
  • curious and excited to apply these in challenging real-world settings
  • thrive in working to deliver impactful solutions to the business problem
  • in the face of ambiguity over which modeling approaches will deliver the best results
  • strong economic expertise
  • interdisciplinary learning
  • work effectively in diverse teams
  • partner with engineering to develop scalable data resources that transform successful models into new products and services
  • writing code to implement a DID analysis or estimate a structural model
  • writing and presenting a document with findings to business leaders
  • understanding their challenges
  • developing a research agenda that will address those challenges
  • helping them implement solutions

Other signals

  • applying causal inference techniques
  • impact estimation
  • large-scale data modeling
  • machine learning
  • attribution models
  • design experiments