Computational Research Analyst

Requisition # 2024-19209
Date Posted 3 weeks ago(5/28/2024 2:48 PM)
Department
Statistics & Machine Learning
Category
Information Technology
Job Type
Full-Time

Overview

The Center for Statistics and Machine Learning is hiring a Computational Research Analyst to perform research on aggregated decision-making through rule systems with includes research into electoral mechanisms including the Electoral College, redistricting, and voting rules. 

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 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. 

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.

Qualifications

  • 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.

Princeton University is an Equal Opportunity/Affirmative Action Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law. KNOW YOUR RIGHTS

Standard Weekly Hours

36.25

Eligible for Overtime

No

Benefits Eligible

Yes

Probationary Period

180 days

Essential Services Personnel (see policy for detail)

No

Physical Capacity Exam Required

No

Valid Driver’s License Required

No

Experience Level

Associate

#LI-JJ1

Options

Sorry the Share function is not working properly at this moment. Please refresh the page and try again later.
Share on your newsfeed

Connect With Us!

Join our Talent Network to receive updates about working at Princeton.

Princeton University job offers are contingent upon the candidate’s successful completion of a background check, reference checks, and pre-employment screening, as applicable.


If you have questions or comments regarding the iCIMS Privacy Policy or iCIMS FAQs, please contact accounts@icims.com.


Go to our careers site.