Computational Research Analyst

Prof. Sam Wang performs research on aggregated decisionmaking through rule systems. This work includes research into electoral mechanisms including the Electoral College, redistricting, and voting rules. As part of these efforts, the Princeton Gerrymandering Project is recruiting a Computational Research Analyst.

The Computational Research Analyst will develop computational analysis of redistricting and voting rules, toward the goal of performing analytics and scholarship relevant to identifying the performance characteristics and inefficiencies of U.S. democracy. The work will be made publicly available through peer-reviewed scientific scholarship as well as publicly available databases that may be of use to a variety of audiences. 

A principal duty will be the updating and maintenance of the Princeton Gerrymandering Project, a comprehensive resource for Congressional and legislative redistricting. The work will include dissemination and archival of codebooks, scripts, map content, and analytics. Other work includes the investigation of electoral rules such as ranked-choice voting and other modifications, with the goal of quantifying functional impacts. Translation to general audiences is part of the work and will produce content that is understandable to nontechnical readers. This comes in addition to other scholarship in scientific, statistical, and law journals.

This position is suitable for someone with graduate or postgraduate level competence in one or more relevant subject areas, including computational simulation, model testing, and geospatial analysis. 

The term of this appointment is 1 year, with the possibility of renewal based upon satisfactory performance and funding. 

• Maintain and expand a high-quality database of computationally-driven analysis of redistricting plans for all 50 states combining census data, precinct-level results, and other information using Python (including numpy) and GIS software.
• Publish codebooks and datasets to allow public access to analysis, and to drive legal and academic scholarship.
• Perform original computationally-intensive research on ranked-choice voting and other proposed changes to U.S. electoral institutions.
• Conduct technical analysis for state and local-level partner organizations that are working on redistricting.
• Coordinate with nonprofit organizations and collaborators in several states.
• Cultivate collaborations with Mapbox and other platforms that can contribute to the build-out of Representable and the Princeton Election Consortium.

• This position requires a bachelor’s degree in computer science, statistics, or physics with 2+ years of experience.  More experienced applicants are also welcome. 

• Strong quantitative and programming background (Python, QGIS)

• A willingness to learn GIS software and other programs or tools necessary for the project

• Experience gathering and combining data from many disparate sources

• An interest in law, government, or democratic reform

• Ability to balance and work on several projects simultaneously and successfully 

• Strong orientation toward teamwork and collaborative research

Preferred Qualifications 

• Background in high-performance computing (C, C++, or a comparable language) a plus. 
• Excellent writing and verbal presentation skills also highly desired.

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