Mercy Corps

Mercy Corps

Geospatial Analytics & Mapping Intern (US-Based Applicants ONLY)

United States · Intern · Internship

Sponsorship not specifiedDetected 3 days ago
PythonFull-Stack DevelopmentGitAWSAzureCloud PlatformsData ScienceGISCommunicationCritical ThinkingRemote Sensing

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16Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • Mercy Corps (rebranding as Prosper Global in September 2026) is a leading global organization powered by the belief that a better world is possible.
  • The field of GIS, Earth Observation and Artificial Intelligence is growing rapidly within the humanitarian community.

Responsibilities

  • In disaster, in hardship, in more than 30 countries around the world, we partner to put bold solutions into action - helping people triumph over adversity and build stronger communities from within.
  • The Geospatial Intern will support the objectives of the

Requirements

  • Mercy Corps Headquarters: 45 SW Ankeny Street, Portland, Oregon 97204 Mercy Corps: https://www.mercycorps.org/ In your statement, you are encouraged to discuss any relevant experience, and demonstrate your interest.

Nice to have

  • APPLICANTS FOR THIS INTERNSHIP MUST BE BASED IN THE UNITED STATES.
  • If you have one, we would love to see a link to your online portfolio or GitHub on your resume.
  • If you do not, please include a few examples of maps (static or dynamic) or other geospatial applications that you have created or contributed to with your application.
  • We foster a culture of trust and respect, where every team member is valued for their contributions, empowered to reach their full potential, and motivated to do their best work.
  • We will not tolerate child abuse, sexual exploitation, abuse, or harassment by or of our team members.
  • Mercy Corps Headquarters:
  • SW Ankeny Street, Portland, Oregon 97204 Mercy Corps:
  • https://www.mercycorps.org/ In your statement, you are encouraged to discuss any relevant experience, and demonstrate your interest.

Skills

  • Analytics & Mapping Intern (US-Based Applicants ONLY) INTERN WORKSITE LOCATION:
  • August 2026 -
  • December 2026 HOURS PER WEEK:
  • Data Science FUNDING AVAILABLE FOR THE INTERNSHIP:
  • $1603.50/month (part-time: 20 hours/week) Mercy Corps responsibilities
  • For the duration of this internship assignment the intern will receive:
  • Intern will remain responsible for all costs associated with:
  • Laptop for working remotely
  • This may be analysis using desktop software (e.g. QGIS, ArcGIS) or programmatic workflows (Python, Google Earth Engine, etc).
  • Some examples of possible projects could include:

Compensation

  • Team members are required to complete mandatory Code of Conduct elearning courses upon hire and on an annual basis.

Company info

  • We are committed to the core principles regarding prevention of sexual exploitation and abuse laid out by the UN Secretary General and IASC.

Equal opportunity

  • Mercy Corps is an equal opportunity employer committed to providing equal employment opportunities to all employees and qualified applicants for employment without regard to race, color, sex, sexual orientation, religion or belief, national origin, age, disability, marital status, veteran status, or any other characteristics protected under applicable law.
  • Safeguarding & Ethics Mercy Corps is committed to ensuring that all individuals we come into contact with through our work, whether team members, community members, program participants or others, are treated with respect and dignity.
  • As part of our commitment to a safe and inclusive work environment, team members are expected to conduct themselves in a professional manner, respect local laws and customs, and to adhere to Mercy Corps Code of Conduct Policies and values at all times.
  • Team members are required to complete mandatory Code of Conduct elearning courses upon hire and on an annual basis.
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This listing is sourced directly from Mercy Corps's careers page and normalized into a canonical job model.