Full Time Term Research Assistant - 6 month position

Ellora Derenoncourt (Princeton University) and Joan Martinez (UC Berkeley) are seeking a full-time research assistant to work on projects related to racial inequality for a period of six months. The preferred start date is October 30, 2024; however, earlier, or later is feasible. Rolling applicant reviews begin on October 22, 2024, and continue until the post is filled.

Additional details

The position will involve working closely with the project PIs on new and ongoing applied microeconomics studies in the areas of labor economics and economic history, focused on racial inequality. The position will entail using advance natural language processing methods to extract information from OCR-digitized historical sources. Proficiency and experience with transforming models, supervised learning and contextual embeddings is required. The RA will also provide assistance with the compilation of historical sources and the assembly of datasets. 

A strong quantitative background, strong computational abilities, including programming, the capacity to work independently to solve issues, and a long-term interest in economics research are required. Candidates with computer science and data science backgrounds are strongly encouraged to apply. A background in economics is helpful, but not required. 

Qualifications

• Applicants should have an undergraduate or masters in Computer Science, or Data Science by January 1st, 2024. PhD students in these fields are strongly encouraged to apply.
• Experience carrying out rigorous research using the following methods: 
  • supervised learning for classification (SVM, BERT), transforming models, and contextual embeddings
  • advance programming and computational methods in natural language processing in Python and R,
  • OCR recognition of large datasets, machine learning techniques for text extraction in Python (pytesseract, layout, pyocr, ocrodjvu, and similar packages)
• Experience using computer cluster facilities for handling large datasets (linux)
• Demonstrated background in programming experience with R and Python
• Highly organized, professional, detail-oriented and motivated
• Demonstrated excellence in written and oral communication.

Required application materials

• Cover letter
• CV
• Unofficial transcript
• Coding sample 

 The NBER is an Equal Opportunity Employer. We do not discriminate on the basis of race, religion, color, sex, age, national origin or disability. The NBER will make reasonable accommodation for any disabled applicant, and will provide assistance to disabled applicants as needed during the application process.