The College of Nursing (CON) at the University of Tennessee Knoxville, the state’s flagship research institution, seeks a health and education policy research associate to join an interdisciplinary research team under the direct mentorship of Clea McNeely, DrPH. The research associate will contribute to an applied research project funded by the William T. Grant Foundation examining school attendance, chronic absenteeism, and truancy using linked longitudinal administrative data from Maryland’s education and juvenile justice systems (Maryland Longitudinal Data System). The project emphasizes identifying “bright spot” middle and high schools that sustain strong attendance outcomes for Black, Hispanic, and economically disadvantaged students—while reducing punitive responses to absenteeism (e.g., unexcused absence labelling, in-school suspensions, truancy petitions to court).

This is a grant-funded position and is contingent upon the continued funding of the grant.

REQUIRED QUALIFICATIONS:

Education:

• Earned Master’s degree in economics, public health, public policy, or a related field

Experience:

• Demonstrated experience with causal inference methods.
• Experience working with large datasets and writing clean, well-documented code.

Knowledge, Skills, Abilities:

• Advanced programming skills in Stata, Python, or R (at least one at an advanced level).
• Excellent communication, organizational, interpersonal, and time-management skills.
• Must be able to pass required background/security checks prior to data access.

Applicants must be legally authorized to work in the United States on a full-time basis without need now or in the future for sponsorship for employment-based visa status.

Preferred Qualifications:

• Earned Doctorate
• Interest in education research.
• Interest in dissemination of research findings to practice audiences.

Work Location: This position can be distance/remote or onsite with check-ins twice a week and milestone-based deliverables.

Duration: 12 months, with possibility of extension up to 24 months.

Compensation:

• UT market range: MR10
• Anticipated hiring range: $60,000
• Find more information on the UT Market Range structure here

Application Instructions

To express interest, please submit an application with the noted below attachments. To be assured of full consideration, completed applications with all requested materials should be submitted on or before August 1^st.

• Letter of interest / short cover letter (1 page) describing relevant experience with:
  • Large dataset management
  • Matching methods and DiD studies
  • Data visualization
  • Interest in education policy research
• CV
• Names/contact info for at least 2 references

About the College of Nursing

With a longstanding tradition of excellence in nursing education, research, and clinical practice, we are proud to be a guiding force in healthcare education. We embrace innovation while honoring the values that have shaped our success. By offering cutting-edge academic programs, supporting impactful research, and forging strong community partnerships, we prepare healthcare professionals who deliver high-quality, culturally responsive care in a diverse and ever-changing world. Join the Vol Nurse Family! Get to know CON

Data Management & Reproducible Workflows

• Build, document, and maintain reproducible data pipelines (cleaning, merging, versioning).
• Work with large administrative, multi-level, linked datasets; implement QA checks and validation.
• Create codebooks and data documentation for internal use and publication.

Econometric Analysis / Causal Inference

• Create matched samples of schools and students (e.g., propensity score matching, exact/coarsened exact matching, weighting).
• Implement quasi-experimental methods such as dynamic difference-in-differences (DiD) and synthetic DiD using matched samples.

Visualization & Research Communication

• Produce publication-quality figures (event-study plots, balance diagnostics, distributions; maps if relevant).
• Assist in drafting methods/results text, tables, and appendices as needed.

REQUIRED QUALIFICATIONS:

• Earned Master’s degree in economics, public health, public policy, or a related field
• Demonstrated experience with causal inference methods.
• Advanced programming skills in Stata, Python, or R (at least one at an advanced level).
• Experience working with large datasets and writing clean, well-documented code.
• Excellent communication, organizational, interpersonal, and time-management skills.
• Must be able to pass required background/security checks prior to data access.