Computational Research Analyst

Princeton University - Princeton, NJ

Full-time - Entry Level

Princeton, NJ

Educational Services

About the position

The Center for Statistics and Machine Learning at Princeton University is seeking a Computational Research Analyst to engage in research focused on aggregated decision-making through rule systems. This role encompasses a variety of tasks, including the examination of electoral mechanisms such as the Electoral College, redistricting, and voting rules. The primary objective of the Computational Research Analyst will be to develop computational analyses of redistricting and voting rules, aiming to identify performance characteristics and inefficiencies within the U.S. democratic system. The findings from this research will be disseminated through peer-reviewed scientific publications and made accessible via publicly available databases, catering to a diverse audience. A significant responsibility of this position will involve the updating and maintenance of a comprehensive resource dedicated to Congressional and legislative redistricting. This includes the dissemination and archival of codebooks, scripts, map content, and analytics. The role will also require investigating electoral rules, such as ranked-choice voting, to quantify their functional impacts. An essential aspect of the job will be translating complex findings into content that is comprehensible to non-technical audiences, contributing to broader scholarship in scientific, statistical, and legal journals. The ideal candidate will possess graduate or postgraduate-level expertise in relevant fields, including computational simulation, model testing, and geospatial analysis. This appointment is for a term of one year, with the potential for renewal based on performance and funding availability.

Responsibilities

• 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.

Requirements

• Bachelor's degree in computer science, statistics, or physics.
• 2+ years of experience in a relevant field.
• Strong quantitative and programming background (Python, QGIS).
• Willingness to learn GIS software and other necessary tools for the project.
• Experience gathering and combining data from various sources.
• Interest in law, government, or democratic reform.
• Ability to manage multiple projects simultaneously and successfully.
• Strong orientation toward teamwork and collaborative research.

Nice-to-haves

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

Benefits

• Health insurance
• Paid holidays
• Flexible scheduling
• Professional development opportunities
• Tuition reimbursement