The Research Alliance for New York City Schools, an independent research center housed in the NYU Steinhardt School of Culture, Education, and Human Development, invites applications for a Research Analyst (Technical). The Research Analyst will be responsible for processing and analyzing data from school administrative records, surveys, and assessments as part of a study of New York City’s Computer Science for All initiative. The Computer Science for All initiative aims to ensure that all students have access to high quality opportunities to learn computational thinking, problem-solving, and critical and creative thinking. The Research Alliance is conducting a multi-year study of the initiative’s implementation and impact.

About the Research Alliance for New York City Schools

The Research Alliance for New York City Schools, formed in 2008, is an important and growing part of NYC’s education community. Our mission is to conduct rigorous studies on topics that matter to New York City’s public schools. We strive to advance equity and excellence in education by providing evidence about policies and practices that promote students’ development and academic success. Our access to longitudinal data from both the NYC Department of Education and the City University of New York, coupled with our strong relationships with educators and policymakers, allow Research Alliance staff to investigate meaningful questions about education and equity.

Job Description

  • Work with project leads to develop and implement data processing, storage, retrieval, documentation, quality assurance, and analytic systems for the Research Alliance data archive, which consists of multi-level education data sets.
  • Assist with the development, design, management, and execution of data cleaning, processing, and analysis tasks associated with research projects.
  • Assist with preparation of reports on research findings that meet the highest standards of education science and that provide clear, accessible, relevant information to policymakers and practitioners.
  • Fact-check reports and review SAS code for accuracy.
  • Assist with field-based data collection efforts, which may include conducting structured interviews, focus groups, and program observations.

Requirements and Skills

  • BA/BS degree or higher in education policy, public policy, economics, statistics, applied psychology, sociology, computer science, or related social science field.
  • Demonstrated programming and statistical analysis skills in SAS, or comparable software, and in Microsoft Excel is required. Proficiency in R and Stata is desirable. Successful candidates will be required to learn and become highly proficient in SAS.
  • Experience working on education or related social science research projects.
  • Proficiency in data processing and management.
  • Strong written and verbal communication skills.
  • Ability to take initiative, work independently, and work in a team environment.
  • Ability to manage competing demands and complete tasks within time constraints.
  • Training, coursework, or experience in field-based data collection (e.g., interviews, focus groups, observations) is a plus.
  • Experience and interest in computer science education strongly preferred.
  • Strong organizational skills.
  • Experience and interest in data visualization is preferred.
  • Knowledge of New York City Department of Education and its data systems is desirable.
  • We seek applicants who carry diverse perspectives. Strong candidates are able to recognize how potential social/cultural biases may impact research and policy.

Salary

  • $48,000 annually with a generous benefits package including tuition remission (more information here).

Application Process

  • Submit resume or curriculum vitae and cover letter discussing interest in the position and qualifications.
  • Provide one SAS coding example. If you are proficient in an additional language, please provide a sample for that as well. Other languages may include R, Stata, or Python. Please provide your programming example files in one of the following formats: .do, .r, .txt, .sas, .py 
  • References will be requested after initial interview.
  • Application materials must be submitted online here.
  • Review of applications will begin immediately and continue until the position is filled. We encourage applicants to apply as soon as possible.