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Cambridge, MA

Policy and Communications Fellow – Workforce

Posted July 27, 2022

 

Position Overview

Blueprint Labs is recruiting a Research Fellow based at MIT in Cambridge, MA. Blueprint uses data and economics to uncover the consequences of policy choices and improve society. Based in the Department of Economics at MIT, Blueprint Labs consists of over 18 academic affiliates who represent leading economic thinkers, ten full-time staff members, and six graduate researchers. Blueprint Labs works closely with leading academic, government, and nonprofit institutions across the country to generate pioneering research that informs policy and practice in education, health care, and the workforce.

We are seeking a motivated, independent, and organized Policy and Communications Fellow to support communications and policy work on our workforce initiatives. The Policy and Communications Fellow will work closely with Blueprint Labs Co-Director David Autor, MIT Institute Professor Daron Acemoglu, and our collaborators at other universities. This position also offers the opportunity to work collaboratively with other Blueprint Labs research staff, including a cohort of other fellows. Previous fellows have gone on to master’s and Ph.D. programs or careers in consulting, tech, government, economics, K-12 education, and more. Curious to learn more? Check out “A Day in the Life at SEII” (note that our lab used to be called SEII!).

Fellows receive a full-time, one-year appointment that is renewable annually (contingent on funding). An employment term of two years is strongly preferred. This position will begin as soon as possible, no later than June 15, 2023, in-person at Blueprint’s office in Cambridge, MA. The salary range is $53,000-$58,000.

 

Principal Duties and Responsibilities (Essential Functions**)

The Policy and Communications Fellow will effectively communicate technical labor economics research with MIT faculty, affiliated faculty, and external audiences. Specific responsibilities include: data collection; editing papers, articles, essays, chapters, and other written content for publication; reviewing data analyses to translate research findings; copy-editing; preparing scientific presentations; creating data visualizations; conducting literature reviews and bibliographies, often using tools like BibTex; engaging in discussion in project meetings; and other related tasks. The fellow can expect to write about technical research results for different audiences and assist with efforts to disseminate findings, including events. Other duties will arise as needed. This position requires a high level of independent judgement and presents opportunities for professional development and on-the-job learning. The fellow can expect to work on multiple projects simultaneously, balancing deliverables on different timelines.

This position will be based in-person in our Cambridge, MA office. This position is not eligible for any visa sponsorship.

 

Qualifications & Skills 

MINIMUM REQUIRED EDUCATION AND EXPERIENCE:

  • Education: A minimum of a bachelor’s degree in economics, computer science, mathematics, statistics, or a related field.
  • Experience: Minimum 2 years’ specialized experience with quantitative data analysis, research methods, and/or social sciences research (which may include coursework or experience gained as an undergraduate).
  • Skills: Programming skills, particularly around data analysis, cleaning, and simulations. Previous fellows in this position have used Stata, R, or Python to conduct analyses.
  • Ability to work independently in a self-directed role across multiple projects, managers, and teams.
  • Responsibility and Judgement: Deals with confidential information and/or issues using discretion and judgement.

PREFERRED EDUCATION AND EXPERIENCE:

  • Education: Coursework or experience in labor economics, econometrics, and/or computer science
  • Experience: Knowledge of Stata, Python, and/or R, and previous research experience, acquired through a research assistantship or an independent research project, are strongly preferred. Some background or willingness/ability to learn Stata is particularly important
  • Skills: Familiarity with machine learning and natural language processing methods is preferred, though not necessary

 

How to Apply:

  1. Submit your application via MIT’s hiring site here. If this link doesn’t work, please visit hr.mit.edu, Click “Search Open Positions,” and search for Job Number
    • Please only submit one application to the MIT site.

AND 

  1. Complete the Google form at this link.
    • The Google form requires you to upload a cover letter, resume, and unofficial transcript in a single PDF.
    • For the Fall 2022 hiring cycle, Blueprint is hiring fellows for multiple projects, and candidates are encouraged to apply to the role or roles that best suit your skills and interests. Please note in your cover letter which role(s) you’re applying to and select the appropriate boxes in the Google Form.

The priority application deadline is Sunday, September 18, 2022 at 11:59pm ET. We will continue to accept applications after the priority deadline through Monday, October 31, 2022 at 11:59pm ET.

Candidates who apply to meet the priority deadline and advance to further stages can expect the following additional activities:

    • Late-September: Complete a timed data task and submit a pre-existing writing sample. You will receive about two weeks to access the task, and it must be submitted within 48 hours of starting.
    • Early to mid-October: Participate in interviews with current Blueprint fellows and staff managers.
    • Late-October: Participate in final interviews with faculty and project team members. At this time, we will also request professional references.

 

Please consult our Frequently Asked Questions page to learn more about the hiring process. Questions not addressed in the FAQ should be directed to hiring@mitblueprintlabs.org.

MIT is an equal employment opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, ancestry, or national or ethnic origin.