Poverty & Economic Development Full-time

Data Research Associate - Dartmouth College

Poverty Action Lab (J-PAL)

Location

United States of America

Type

Full-time

Posted

Jan 03, 2026

Compensation

USD 60000 – 60000

Mission

What you will drive

Core responsibilities:

  • Performing high-quality OCR and manual data entry from historical PDF sources into structured databases or spreadsheets
  • Merging newly digitized data with contextual sources and performing statistical analysis on the resulting data set
  • Building reproducible data and analysis pipelines, documentation, and metadata to support public data release
  • Coordinating and providing guidance to undergraduate research assistants working on the projects

Impact

The difference you'll make

This role contributes to economic research focused on gender, labor economics, and development economics, supporting evidence-based policy work that can inform poverty alleviation and economic development initiatives.

Profile

What makes you a great fit

Required skills and qualifications:

  • Bachelor’s degree in Economics or a related field, completed by the start of the appointment
  • Previous programming experience in languages such as R, Matlab, Python, or Stata
  • Demonstrated interest and enthusiasm in applying programming experience to economic research and economic policy work
  • Excellent writing and oral communication skills, attention to detail, and problem-solving abilities

Benefits

What's in it for you

Compensation and benefits:

  • Salary: $60,000 per year plus fringe and benefits
  • Eligibility for Graduate Special Student status with ability to take certain Dartmouth courses (tuition paid by Dartmouth, maximum 1 course per academic year)
  • Possibility of 1-year extension upon satisfactory performance
  • J-1 visa sponsorship for eligible candidates

About

Inside Poverty Action Lab (J-PAL)

The Poverty Action Lab (J-PAL) is a research center focused on reducing poverty by ensuring that policy is informed by scientific evidence through randomized evaluations.