-
government partners. Constructs, trains, and deploys scalable machine learning algorithms for a variety of predictive analytics research projects. Coordinates data collection, econometric analysis and provides
-
relevant to international development. Technical Skills or Knowledge: Knowledge of the practicalities and econometrics of randomized control trials. Working knowledge in complex partnerships including
-
, econometrics preferred. Familiarity with research theory, methods, and literature preferred. Additional Requirements Education, Experience, or Certifications: Education: High school diploma required
-
. The successful candidate will have experience with data management, econometrics, and statistical modeling. The PA will contribute to all aspects of research, including data collection, model development, and
-
. Builds and tests econometric models. Creates tables and figures to communicate findings. Helps with the design of experimental and survey studies. Conducts complex data analysis. Writes and edits research
-
university. Among our faculty are many members of the National Academy of Sciences, the American Academy of Arts and Sciences, and the Econometric Society. The Economics for Everyone Media and Education
-
areas: Accounting, Behavioral Science, Econometrics/Statistics, Economics, Entrepreneurship, Finance, Marketing, Organizations and Strategy, Strategic Management, and Operations Management. Qualifications
-
. Among our faculty are many members of the National Academy of Sciences, the American Academy of Arts and Sciences, and the Econometric Society. Job Summary Reporting to Professor John List in
-
research involves using econometric methods and globally comprehensive data to quantify the impacts of climate change sector-by-sector, community-by-community around the world. It will allow decision-makers
-
before pursing graduate studies, although this can be extended by mutual agreement. Responsibilities Assembles, cleans and manages data sets. Designs and tests econometric models / conducts complex data