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Graduate Intern - Residential Stock Modeling

🚀 Off-cycle Internship


đź’» Remote
🤑 $42.7K - $68.3K

AI generated summary

  • You must have a strong track record in probability, statistics, regression, and forecasting, with experience in geographic resolved datasets and data-driven modeling. Enrollment or recent graduation from a degree program with a minimum GPA of 3.0 is required. Additional qualifications include proficiency in programming languages like R, Python, or MATLAB, as well as familiarity with US census geography and basic thermodynamics knowledge.
  • You will assist in advancing residential building energy model ResStock by gathering, analyzing, and processing large-scale building data to develop geographic sampling methods.

Off-cycle Internship



  • The Residential Buildings Solutions and Scaling Group (RBSSG) is dedicated to fostering residential building decarbonization by bridging industry and innovation through interdisciplinary partnerships and collaboration for tangible impact at scale. We drive adoption of high performance technologies and strategies addressing climate change, enhancing housing resiliency, training the workforce of the future, and improving affordability and efficiency.​


  • The successful candidate will have a track record of developing and documenting methods of probability and statistics, regression, and forecasting for applied problems, preferably with an emphasis in geographic resolved datasets. Additionally, they will have demonstrated evidence of performing high caliber technical work with minimal supervision of day-to-day tasks.
  • Basic Qualifications:
  • Must be enrolled as a full-time student in a Bachelor's, Master's or PhD degree program, or graduated in the past 12 months from an accredited institution. Candidates who have earned a degree may work for a period not to exceed 12 months. Must have a minimum of a 3.0 cumulative grade point average.
  • Additional Required Qualifications:
  • Experience forecasting geographically resolved phenomena.
  • Experience developing and testing data-driven models.
  • Experience performing analytic work in R, Python, MATLAB, or an equivalent programming language.
  • Preferred Qualifications:
  • Experience in probability and statistical analysis, such as Bayesian Statistics
  • Knowledge regarding US census geography
  • Basic knowledge of thermodynamics and building science

Education requirements

Currently Studying

Area of Responsibilities



  • This role will assist in advancing the broader portfolio of the residential building stock energy model ResStock. This role will assist in data gathering, analysis, and processing of large-scale building model data to develop methods of sampling and downscaling the data to higher geographic resolutions.


Work type

Full time

Work mode





42700 - 68300 USD