Gis Python Internship Jobs in North Carolina (NOW HIRING)

American Forest Management (AFM) is seeking a motivated and curious Resource Planning and Biometrics Intern to join the Resource Planning and Biometric Services (RPBS) group at AFM. RPBS is responsible for providing forest planning services which support client land management and forest investment decisions, as well as biometric services necessary for planning projections, inventory analysis, and data systems. This position will be based at our Headquarters in Charlotte, NC or can be remote based and offers a unique opportunity to apply cutting-edge data science methods to real-world forestry challenges. If you’re excited about working with big data, collaborating with some of the largest forestland holders in the country and producing deliverables that matter please consider applying. 

As part of our 2027 Internship Program, interns will also have the exciting opportunity to compete for a $2,500 scholarship. This scholarship will be awarded to an intern who demonstrates outstanding performance, initiative, and alignment with our company values throughout the program. It’s our way of recognizing and rewarding the hard work, creativity, and dedication of our interns while investing in their continued growth and success.

Primary Duties and Responsibilities:

• Apply advanced analytical tools such as R and Python to manipulate, analyze, and visualize complex forest and remotely sensed data. 

• Work with standard data structures and formats (Postgres, SQL, Excel, etc.) to clean, organize, and process large datasets. 

• Develop and test forest growth models, predictive analytics, and decision-support tools to enhance forest management strategies. 

• Automate complex processes using R, Python, and other relevant platforms. 

• Integrate geospatial data (e.g., Sentinel-2, LiDAR) into forest planning and/or forest growth models. 

• Collaborate with experienced biometricians and forest planners to address complex forest management challenges. 

• Build creative and effective solutions that drive better decisions in forest inventory, silviculture, harvesting, and growth modeling. 

• Share insights and tools with the team to build collective capacity. 

Qualifications:

• Bachelor’s degree in forestry, geospatial sciences, or a related field

• Working toward a master's degree (or higher) preferred, ideally in biometrics or a quantitative forestry discipline

• Experience with forest systems (metrics and silviculture) at a bachelor's level or higher

• Strong programming skills in R and/or Python (bonus: if you also work in derivatives such as Jupyter Notebooks, or R Shiny)

• Solid understanding of statistics and quantitative methods. Ability to manage large, messy datasets

• Familiarity with GIS software (QGIS, ESRI), forest growth and yield models (Forest Vegetation Simulator, FVS), or linear programming software (Woodstock Optimization Studio)

• Strong problem-solving skills and creativity 

• Growth mindset

• Ability to work in teams and collaborate with various groups across AFM 

• Professional integrity, someone who does the right thing even when no one is looking

• Ability to work both independently and as part of a team

Additional Perks:

• Exposure to complex forest management data and real-world decision-making 

• Opportunity to work with a team of forestry professionals who are passionate about sustainable management

• The chance to make a real impact on how forests are managed

Compensation: 

•  $18-22 an hour

Additional Information: 

• Office Location – Charlotte, NC or remote
• AFM will not provide financial assistance with housing
• Flexible start and end date
• This position is not eligible for employer-sponsored work authorization. Candidates must be authorized to work in the U.S. at the time of application

Apply by November 1st, 2026, to launch your forestry career with American Forest Management!

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, or national origin.