State Legislative Science, Technology, and Public Policy Fellow

State Legislative Science, Technology, and Public Policy Fellow

• Applicants must submit a resume, cover letter, and writing sample to be considered. Please combine all materials into a single document. The cover letter should explain your interest in the position and highlight relevant skills and experience. The writing sample should be no more than 10 pages and should demonstrate strong professional writing skills. Policy-related samples, such as a policy memo, briefing paper, analysis, report, or similar document, are preferred.
• This is one-year term-limited appointment and renewal for a second year is dependent on continued grant funding.
• This position requires working both in-person in Lansing at the Department of Environment, Great Lakes, and Energy and remotely.

IMPORTANT NOTE:

Please do not use AI tools in any part of the application process (drafting your cover letter, answering questions during the interview, etc). We are interested in assessing your qualifications without the input of AI assistance.

The Science, Technology and Public Policy (STPP) program at the Gerald R. Ford School of Public Policy has recently launched MiST, the Michigan Science, Technology, and Public Policy Fellowship program, which embeds fellows in offices in state government in Lansing, Michigan for twelve months. MiST is currently recruiting for a one-year post‑graduate state legislative fellowship position focused on AI policy and governance as it relates to the priorities and work of the Department of Environment, Great Lakes, and Energy.

The fellow is expected to work hybrid schedule with 1-2 days in-person per week at EGLE's offices for the full appointment.

To ensure a successful fellowship year, the fellow will receive training about Michigan state government, the policy process, policy writing instruction, communicating science and technology to non-scientific audiences, and how to provide evidence-based, objective communications that help expand or clarify technical topics. Staff at EGLE will manage the day-to-day work of the fellow and you will report to Brad Pagratis, EGLE Information Management Division Director.

The MiST fellowship program is administered by the Ford School, one of the nation's foremost policy schools at one of the world's great public universities. We are a community dedicated to the public good. We inspire and prepare leaders grounded in service, conduct transformational research, and collaborate on evidence-based policy making to take on our communities' and our world's most pressing challenges.

To learn more about the Ford School, read About Us.

• Research 35%
  • Conduct literature and landscape reviews on AI governance and other technology related policy topics (e.g., innovation policy, data centers, data governance, data privacy, and responsible AI use and implementation).
  • Track key bills, hearings, new developments at Congress, federal agencies and other states on AI and/or data governance
  • Engage in online research using state government and university library databases and conduct in-person information gathering as needed.
  • Work with team members at EGLE to collaborate on shared research and policy projects
• Communications and Findings Dissemination 20%
  • Draft memos, presentations, briefs, and reports intended for a non-technical audience
  • Assist in creating and maintaining print and web-based resources
  • Present findings to supervisors and staff
• Agency and government relations 10%
  • Respond to queries on technology policy
  • Determine research needs and priorities for the questions posed and communicate them accordingly
  • Practice confidentiality in work communications.
• Data Analysis and AI system analysis 35%
  • Research and develop strategies for large scale data clean-up, data quality, and data governance initiatives
  • Perform data cleaning and merging procedures on complex data sets
  • Review system documentation from AI implementations
  • Review, analyze, and make recommendations on model training data
  • Assist with data analysis, including preparing data analysis results for memos and presentations
  • Review and compare system model cards and model information

• Fellows must…