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Advance Forestry - Data Analysis Intern-01016414

Job Field:
Job Type:
Seasonal, Temporary, Internship
Location Detail:
Job Description:

Weyerhaeuser’s Advanced Forestry Systems (AFS) group is seeking a temporary Advance Forestry - Data Analysis Intern for a data annotation and labeling project, for a duration of 3-3.5 months. The successful candidate, working remotely, will work with AFS biometricians and scientists to annotate geospatial data in preparation for machine learning pipelines. This successful candidate will work remotely.

At Weyerhaeuser, our focus is as much on building our people as building our business. We are committed to creating an environment where individuals can flourish, diversities encouraged, and communities supported. We are one of the premier integrated forest organizations in the world. At Weyerhaeuser, we are always looking for people who can contribute, grow, think, and create!  Our associates are the real reason we have been in business for over 100 years. Their skill and ingenuity have made Weyerhaeuser one of the largest timberland companies and manufacturers and distributors of wood products in the world.

  • Pursuing a Bachelor’s degree or higher in GIS or related field OR a recent graduate.  
  • Minimum Junior class standing.
  • High attention to detail, including precise and effective communication.
  • The tasks will be primarily repetitive in nature and will require the individual to make judgment-based decisions keeping in mind the project guidelines.
  • Familiarity with common Geospatial Data Formats (GeoTIFF, ESRI Shapefile, GeoPackage)
  • Familiarity with open-source GIS tools (QGIS, gdal).
  • Able to commit to a 3-3.5 month internship.

Preferred Skills:

  • Familiarity with lidar.
  • Experience working with machine learning data labeling pipelines.
  • Experience working with AWS.
  • Experience with git, Python, R, or similar programming languages.
  • Experience labeling lidar point-clouds for segmentation.
  • Experience training and running machine learning models.