Posted by Join Cargill’s global team of 160,000 employees who use new technologies, dynamic insights and over 154 years of experience to connect farmers with markets, customers with ingredients, and people and animals with the food they need to thrive.
Cargill Operations Research Internship (Open to Remote)
Internships at Cargill:
This position is part of the Engineering and Data Sciences team. Your project will connect you to the business where you will interact with a multidisciplinary team of plant managers, process control engineers, etc. You will bring strong technical skills to our data science capabilities. You will explore, connect, and mine data; plus develop models using algorithms for mathematical optimization and build discrete event simulations. In this position you will be part of the Operations Research Capability where you will be working to solve scheduling, logistics, formulation, assignment, and forecasting problems.
Principal Accountability:
You will be assigned to one or two core projects that will be substantive in nature. You will be the primary technical contributor for work on the project, with leadership support from the Engineering and Data Science team. Interns will have the opportunity to:
Gain immediate hands-on work experience in one of the world’s largest agribusiness companies.
Receive periodic and candid feedback on job performance.
Qualifications:
Must be currently enrolled in a MSc or PhD program in Engineering, Data Science, Computer Science, Statistics, Mathematics, Physics, or related STEM fields with a graduation date of December 2023 or later.
Ability to complete a 12-week internship in the summer (May/June – August 2023)
Demonstrated analytical and problem-solving skills
Strong background in statistics, discrete math, and linear algebra
Experience in developing and testing optimization and simulation models
In-depth knowledge of various classes of optimization problems, e.g., linear, integer, stochastic, network, and simulation modeling, e.g., discrete event simulation, agent-based modeling, Monte Carlo simulation, system dynamics.
Proficiency in Python (e.g., pandas, scikit-learn, matplotlib, NumPy)
Ability to understand complex and ambiguous business needs and apply the right tools and approaches
Interested in learning model deployment in a production setting
Curious, self-motivated, driven, and have a passion for problem solving