Urban Heat Data Science Intern (Hybrid)

Job Description

About the Program:

The Ross Center for Sustainable Cities (https://www.wri.org/cities) builds on WRI’s global and local experience in urban planning and mobility and uses proven solutions and action-oriented tools to make the fast-growing urban environment more resilient to new challenges. Our Data for Cool Cities (https://www.wri.org/initiatives/data-cool-cities) initiative aims to combat extreme heat in urban areas. Not only is heat magnified in cities due to the urban heat island effect, cities also are home to most of the world’s population, and vulnerability to heat arises from complex interactions between social, physical, and climatic systems. The Global Cities Data & Tools team is mapping the potential for heat-resilient infrastructure like street trees and cool roofs and modelling the impacts of implementation on the variation of thermal comfort within neighborhoods. By providing innovative data, enabling insights, and promoting sustainable infrastructure choices, this project seeks to improve urban livability, encourage effective governance, and enhance equity.

This is a hybrid position which requires 8 days per month in the office. You can be based in our office in Washington DC Office. Existing work authorization is required at the time of application submission as WRI is unable to sponsor any visa work sponsorship for this position. To be eligible for this position, you must be a resident of DC, Maryland or Virginia at the start of employment.

What you will do:

You will be responsible for integrating equity into your work. In this role you will work to support our urban heat modeling program by conducting independent research on how the results of a local-scale heat model vary across diverse climates and urban environments. The primary tasks include validating input and output data; running the urban heat model and documenting the modeling workflow; and summarizing the outputs and extracting insights.

In particular, you will:

• Compare microclimatic conditions for extreme heat in diverse cities
• Investigate differences in heat exposure and impacts of mitigation strategies
• Augment model documentation with insights about process & performance in diverse geographic contexts

At the end of your internship, you will have the opportunity to present your findings to our Cities Data Community of Practice. You will be supported by the Global Cities Data & Tools team and will report to Drs. Elizabeth Wesley and Ruth Engel.

Internship Learning Outcomes:

• Validating geospatial data and products.
• Implementing the urban heat model and documenting a reproducible workflow.
• Designing research to extract key insights on heat exposure in cities and model performance.
• Reporting results in written, tabular, and visual form.

What you will need:

• Currently pursuing or recently completed a bachelor’s or master’s degree.
• Ability to read and write Python and basic familiarity with R. Experience with Git preferred.
• Proficiency with geographic data science and geospatial analysis including both raster and vector data formats and spatial statistics. Experience with QGIS preferred.
• Experience using physical or statistical models to produce spatio-temporal data preferred but not required.
• Strong written communication skills.
• Interest in applied research to address the challenge of extreme heat in cities.

Compensation

The pay for this internship opportunity is $20/hour for undergrads; $22/hour for graduates and $24/hour for PhD students.

Length of Contract:

This is a full-time, 3-month internship with a flexible start and end date.

Location:

• This is a hybrid internship that will be based out of our Washington D.C. office.
• You will be required to work in the Washington DC office at least 8 days per month.

How to Apply:

Please submit a resume with a cover letter by March 18, 2025. Applicants must apply through the WRI Careers portal to be considered. We may close for applications sooner if we receive a high volume of applications from qualified candidates.