Postdoctoral Research Associate - Integrated Building Deployment and Analysis Group

Overview

We are seeking a Postdoctoral Research Associate who will work with a multi‑disciplinary group of experts in economics, engineering, software development, and building energy modeling on the economic and policy aspects of residential building retrofitting. The candidate will support econometric evaluation of the WAP implementation expectations and results. To accomplish the task, the candidate will work directly with WAP and software experts to identify, gather, curate, and analyze novel datasets not previously available to researchers.

This position resides in the Integrated Building Deployment and Analysis group in the Buildings Materials & System Integration Section, Building and Transportation Science Division, Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL).

• Interacting with a team of experts at ORNL on projects evaluating the U.S. Department of Energy Weatherization Assistance Program and its efficacy in delivering the intended residential energy cost and other household direct benefits.
• Data collection, processing/curation, and analysis.
• Development and implementation of econometric analysis (e.g., difference‑in‑differences).
• Contributions to reports, presentations, and peer‑reviewed journal articles.
• Collaborate with ORNL scientists involved in these efforts, to gain expertise in the economic analysis of energy program delivery relevant to the mission of the U.S. Department of Energy.
• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace.

• Ph.D. in Energy Economics or related Applied Economics discipline with a strong quantitative focus.
• Demonstrable knowledge of applied econometrics and statistical evaluation methods, such as:
  • Difference-in‑Differences (DiD)
  • Fixed‑effects panel models
  • Matching methods (PSM, CEM, nearest‑neighbor)
  • Regression‑based normalization
  • Longitudinal and cross‑sectional data analysis.
• Experience working with large administrative or program datasets.
• Proficiency in statistical programming and data analysis tools:
  • R, Python, Stata, or SAS
  • SQL/database management preferred.
• Ability to clean, harmonize, and analyze complex datasets from multiple sources.
• Experience developing reproducible analytical workflows and documentation.
• Demonstrable specialized communication skills:
  • Proficient technical and scientific writing
  • Publication record in peer‑reviewed journals or technical reports
• Ability to present findings to technical and non‑technical audiences.
• Ability to work independently and interact with a multi‑disciplinary group.

• Familiarity with federal data governance, privacy protection, and anonymization practices.
• Experience collaborating with multidisciplinary teams, state agencies, utilities, or national laboratories.
• Experience with policy evaluation and analysis using economic models.
• Experience with visualization and interactive application development in R and Python.
• Author or co‑author of peer‑reviewed literature publications.

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

For employment at Oak Ridge National Laboratory (ORNL), a Real  form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD‑12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post‑employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check…