Agriculture Intern, Data Analytics

Job Description Summary

Participate in a paid internship program built around your career goals and hands-on projects that impact the organization. As part of the Lamb Weston University Recruiting Internship Program, Interns work towards completing a Capstone project and are assigned a mentor who will guide and evaluate performance. Whether you are interested in Agricultural Science, Data Science, Finance, Supply Chain, IT, Manufacturing, Food Science, Marketing, or Engineering, our highly selective summer intern program has projects that will help you build the next stage in your career.

• Paid internship, May – August (actual dates may vary)
• Gain real-world working experience through dynamic projects assigned by your mentor and assigned department
• Network and learn from executives, department leaders, high performers, and fellow interns across the business
• Receive guidance and feedback from your assigned mentor throughout the program
• Professional development events providing tools that will help after the internship and after graduation

Job Description

The Agriculture Intern, Analytics Intern will be a contributing member of a team responsible for crop analytics across North America supporting critical decision-making related to the sourcing of billions of pounds of potatoes.

This intern is a resource to the Sr. Manager Ag Analytics and will help progress our crop analytics and reporting capabilities by building data visualizations, automating data pipelines, and improving predictive crop modeling. The intern will gain insight into numerous datasets used for crop analytics including weather data, input pricing, field sampling, potato quality, and yields. This internship offers the opportunity to gain experience with real-world applications of data science curriculum. The intern will be working in collaboration with other members across the North American Ag Services team.

Duties & Responsibilities include:

• Automating Data pipelines within our department’s various systems.
• Improve yield modeling training and execution through automated ETL process.
• Build API connections to relevant internal and external Ag datasets.
• Build Power BI dashboards.
• Import open-source satellite imagery.