Research Data Scientist

University of Chicago

University of Chicago

Data Science
Chicago, IL, USA
Posted on Tuesday, January 23, 2024


EPIC - Climate Impact Lab (CIL)

About the Department

As an interdisciplinary research and training center at the University of Chicago, EPIC is contributing innovative solutions to the global energy challenge. Previous results from EPIC projects have been influential with policymakers worldwide and featured in the New York Times, Wall Street Journal, and other leading publications.

Job Summary

This role participates in scientific research projects. Ensures compliance of research activities with institutional, state, and federal regulatory policies, procedures, directives and mandates. Analyzes possible solutions using standard procedures. Writes articles, reports and manuscripts. Assists in drafting presentations on research findings.

Successful candidates will work with the Climate Impact Lab, a unique collaboration of experts—economists, climate scientists, and computational scientists—working to build data-driven estimates of the local and global costs of climate change. Our research involves using econometric methods and globally comprehensive data to quantify the impacts of climate change sector-by-sector, community-by-community around the world. It will allow decision-makers in the public and private sectors to understand the risks climate change presents and mitigate those risks through smarter investments and public policy. The research has also been used to produce the world’s first empirically-derived estimate of the social cost of carbon — the cost to society from each ton of carbon dioxide emitted. This figure will be continuously updated as research progresses and can serve as the basis for energy and climate policies.


  • Develops and enhances climate and impact research tools.
  • Manages and optimizes petabyte-scale data storage systems.
  • Collaborates with researchers for new analyses with large datasets.
  • Trains research assistants in tool usage and provides support.
  • Oversees code management and organization within the lab.
  • Ensures efficient data storage accessibility and integrity.
  • Contributes insights to research and produces output for policymakers.
  • Develops, maintains, and extends computational tools and data storage systems for climate and impact research.
  • Supports researchers in conducting new analyses by working with petabyte-scale in multiple computing environments.
  • Trains research assistants in the use of new and existing computational tools.
  • Acts as a point of contact for code management and data storage at the Lab.
  • Contributes to research on the impacts of climate change.
  • Produces output for policymakers and publication in media outlets.
  • Supports data collection and analytical needs of research projects. Conducts literature reviews and helps write reports and manuscripts. Ensures project compliance with different policies, procedures, directives, and mandates.
  • Takes responsibility for the following non-laboratory duties: transcribing and coding data; developing data collection instruments; presenting research; and recruiting and scheduling research subjects. Acquires higher-level skills and knowledge in the process.
  • Performs other related work as needed.

Minimum Qualifications


Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.



Preferred Qualifications


  • Research-related work in economics, climate science, data science, computer science, or a similar quantitative field is highly desirable.
  • Statistics or econometrics training.


  • ​Bachelor's degree in a related field.

Technical Skills or Knowledge:

  • Strong background in Python; data analysis tools (pandas, xarray), climate data storage formats (e.g. NetCDF, Zarr)
  • Familiarity with version control (git)
  • Experience with geospatial and temporal data visualization.
  • Strong background in distributed computing tools (dask).
  • High-performance computing clusters.
  • Linux command line/bash scripting.
  • Familiarity with R and/or STATA.

Preferred Competencies

  • Strong communication skills.
  • Strong multi-disciplinary collaboration skills.
  • Experience training team members with software tools.

Application Documents

  • Resume/CV (required)
  • Cover Letter (required)

When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

Job Family


Role Impact

Individual Contributor

FLSA Status


Pay Frequency


Scheduled Weekly Hours


Benefits Eligible


Drug Test Required


Health Screen Required


Motor Vehicle Record Inquiry Required


Posting Statement

The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, national or ethnic origin, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

Staff Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.

We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages a diversity of perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange.

All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

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