Data Science Intern

Data Science Intern

Date: Feb 28, 2026

Location: Chesterfield, MO
-Missouri, US, 63017

City : Chesterfield State : Missouri (US-MO)
Country : United States (US)
Requisition Number : 43145

About Bunge’s Global Economic Analysis Team:

Leveraging our inherent market intelligence is a critical component to Bunge’s success, particularly in the dynamic agricultural markets. This is the reason why Bunge has one of the large economic analysis teams in the industry. Our analysis team is comprised of over 50 analysts world‑wide who gather, analyze, supply and demand and other pertinent information. The global analysts work closely with global traders to help market develop market theses that drive the company’s trading and risk decisions.

The team covers global grains, oilseeds, biofuels, ocean freight and livestock.

This internship is scheduled for Summer 2026 (May-August) in St. Louis MO. It offers an extraordinary opportunity to dive deep into how sophisticated economic research, powered by advanced data science, translates into actionable commercial decisions and robust risk management within a leading global agricultural trading powerhouse.

As an integral part of our Data Science team within Global Economic Analysis, you will be at the forefront of innovation, leveraging modern cloud computing tools and advanced statistical methods to extract insights from vast internal and external datasets. You'll actively contribute to the development and maintenance of critical predictive models that forecast global commodity market dynamics, including crop production, pricing trends, and customer behavior.

This hands‑on experience will directly advance our economic research functions worldwide, providing forecasts that influence multi‑million‑dollar decisions. Plus, the internship will equip you with experience of applying best‑in‑class data science practices to solve real‑world, high‑impact problems in the agricultural industry.

• Collaborate on the development and refinement of various predictive models, focusing on critical aspects like crop production forecasts, pricing trend analysis, and customer behavior insights. (Specific projects will be tailored to your skills and interests closer to the internship start, ensuring a meaningful and engaging experience).
• Take ownership in enhancing relevant analytical & data pipelines, with the potential to design and implement optimizations using Python and cloud computing tools.
• Undertake and deliver a significant analytical project related to the team's core objectives, culminating in a comprehensive final presentation to the economic research team, showcasing your achievements and insights gained throughout the internship.

• Minimum MS degree in Ag‑Economics, Economics, Data Science, Statistics, Computer Science, Finance, Engineering, or a closely related quantitative discipline.
• Hands‑on experience with Python and familiarity with its essential analytical libraries (e.g., Pandas, Num Py, Scikit‑learn, Matplotlib/Seaborn) for data manipulation, statistical analysis, machine learning, and data visualization.
• Strong SQL skills for data extraction, manipulation, and analysis from relational and non‑relational databases. Understand version control systems, particularly Git, for collaborative development and code management.
• Proactive self‑motivation with the ability to work independently and to deliver solutions in a fast‑paced, collaborative environment.

• Experience with geospatial data analysis and deep learning for statistical modeling.
• Experience with big data technologies and cloud‑based data platforms and products (e.g., Google Cloud Platform, AWS).
• Specific knowledge of agricultural commodity markets (e.g., grains, oilseeds, biofuels), agronomics, etc.

At Bunge (NYSE: BG), our purpose is to connect farmers to consumers to deliver essential food, feed and fuel to the world. As a premier agribusiness solutions provider, our team of ~37,000 dedicated employees partner with farmers across the globe to move agricultural commodities from where they’re grown to where they’re needed—in faster, smarter, and…