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Intern - Economics, Fees Science

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25d ago

šŸš€ Off-cycle Internship


AI generated summary

  • You: PhD student in Economics, 3rd year or more, skilled in SQL, UNIX, data analysis, econometrics, Python, detail-oriented. Join Amazon as Fees Science Intern!
  • You will learn existing LLM frameworks and causal inference tools, analyze business use cases, read literature, deploy models, design experiments, and collaborate with stakeholders to deliver high-impact analyses and documentation.

Off-cycle Internship



  • We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus.
  • These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement.


  • PhD student in Economics (enrolled in 3rd year or more and not currently on the job market)
  • Preferred Qualifications:
  • Experience with SQL
  • Familiarity with UNIX
  • Experience working with large data sets
  • Knowledge of econometrics
  • Familiarity with Python
  • Attention to detail

Education requirements

Currently Studying
Third Year

Area of Responsibilities



  • Learn the details of both existing LLM frameworks and state of the art causal inference tools leveraged by the Fees Science team. Dive deep into individual business use cases within the fees team. Deliver analyses of high-impact and accuracy that marry causal techniques with natural language prompts and outputs.
  • A day in the life:
  • Reading papers in the causal and LLM literature. Deploying models and programming within Unix systems and AWS tools. Designing new experiments and datasets to be run in the framework they will develop, and checking output with scientists and business stakeholders. Writing high quality documentation and code artifacts.


Work type

Full time

Work mode