Associate Professional Specialist for Princeton Industrial Relations Section-Dobbie Team

Princeton University; Princeton (NJ); On-site; USD 65,000 - 70,000; Full time

Job summary

Princeton University’s Industrial Relations Section seeks an Associate Professional Specialist to contribute to Professor Will Dobbie’s research for 2027–2028. The role centers on developing and managing administrative datasets, building machine learning models, and maintaining a multi-module codebase.

The work is primarily on campus with occasional research trips; this is a one-year term with potential renewal.

Qualifications

• Masters degree in Economics, applied Mathematics, CS, Statistics, or related field.
• Experience with real-world data sets, including education data.
• Graduate coding/algorithm skills; proficiency in Stata, R, Python, MATLAB, SAS, LaTeX.
• Proficiency in quantitative text analysis methods.
• Experience with large language models in research context.
• Strong written and oral communication skills.
• Excellent organizational and time management skills.
• Proficiency in Italian is a plus.

Responsibilities

• Constructing and managing administrative datasets in high-stakes settings.
• Building advanced machine learning models using structured and unstructured data.
• Maintaining a multi-module codebase; working with data models.
• Conducting applied econometrics tasks in Stata or R; generating publication-ready tables and figures.
• Coordinating with third-party contractors and software engineers.
• Producing detailed reports, policy briefs, and presentations for publication.
• Coordinating and submitting IRB review applications.

Skills

Strong written and oral communication

Excellent organizational skills

Time management

Proficiency in Italian (plus)

Graduate coding/algorithm skills

Data handling

Quantitative text analysis

Team collaboration

Education

Masters degree in Economics/related field

Tools

Stata

R

Python

MATLAB

SAS

LaTeX

Large language models

Job description

Associate Professional Specialist for Princeton Industrial Relations Section-Dobbie Team

Location: Princeton, NJ

Salary Range or Pay Grade: $65,000 - $70,000

Description

The Industrial Relations Section (IRS) and the Department of Economics seek a highly motivated and experienced specialist to contribute to the research of Professor Will Dobbie during the 2027-2028 academic year. Dobbie’s research interests focus on causes and consequences of poverty to understand how we can give individuals from all backgrounds a better chance of success. His recent work has examined racial discrimination in the criminal justice system, the changing nature of economic mobility, and how to build more human-centered AI. Appointment will be at the rank of Associate Professional Specialist.

Responsibilities include:

• Constructing and managing administrative datasets in a variety of high-stakes settings, including criminal justice and child protective services
• Building advanced machine learning models using both structured and unstructured data to explore human-AI complementarities
• Maintaining a multi-module codebase; working with Pydantic or similar data models
• Conducting applied econometrics tasks in Stata or R, such as randomization and balance checks, regressions with extensive controls and fixed effects, and generating publication-ready tables and figures.
• Managing and providing quality control of project work completed by third-party contractors, including software engineers
• Producing detailed reports, prepare policy briefs and presentations, and assist with drafting materials for publication
• Coordinate and submit applications for review by Princeton’s Institutional Review Board

The work location for this position is primarily in-person on campus at Princeton University with occasional research trips to Italy. This position is subject to the University's background check policy. This is a one-year term position with the potential for renewal for a second term.

This position is not eligible for sponsorship of an H-1B visa requiring consular processing; other visa sponsorships (including H-1B visas not requiring consular processing) may be available, as appropriate.

Qualifications

• Masters degree in Economics, applied Mathematics, Computer Science, Statistics, or related discipline, or equivalent combination of training, experience, and accomplishments
• Prior research experience in the fields of Economics
• Experience working with real-world data sets, including education or school administrative data
• Graduate-level coding/algorithm skills and proficiency in statistical programming, especially Stata, R, Python, MATLAB, SAS, LaTeX
• Proficiency in quantitative text analysis methods
• Proficiency in, or experience with, large language models in a research context: prompt design, structured output, and evaluating model output
• Strong written and oral communication skills
• Excellent organizational and time management skills
• Proficiency in Italian and knowledge of Italian school system and institutions is a plus

Equal Employment Opportunity Statement

Princeton University is an Equal Opportunity 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, and any other characteristic protected by law.

Pay Transparency Disclosure

The University considers factors such as (but not limited to) the scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University’s good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

The University also offers a comprehensive benefits program to eligible employees. Please see link for more information.

Equal employment opportunity, including veterans and individuals with disabilities.

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