Data Scientist, Senior

Cargill is a family company committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. We sit at the heart of the supply chain, partnering with producers and customers to source, make and deliver products that are vital for living. By providing customers with life’s essentials, we enable businesses to grow, communities to prosper, and consumers to live well.

This position is in our Food Enterprise where we are committed to serving food manufacturers, food service customers, and retailers with a complete range of innovative ingredients and branded products. Our portfolio includes poultry, beef, egg, alternative protein, salt, oils, starches, sweeteners, cocoa and chocolate.

Job Purpose and Impact

The Data Scientist, Senior job plans and leads the development of artificial intelligence models from design and prototyping through deployed solutions to drive decision making. With minimal supervision, this role extracts and integrates complex data from various sources, builds advanced models suitable for the business use case and evaluates model performance for accuracy, scaling and deployment. This job also develops compelling and clear communication materials to facilitate partner on board. By leveraging diverse and expansive data sources, this role applies advanced forecasting, optimization, and machine learning techniques to transform how decisions are made across the supply chain. The Senior Professional accelerates AI adoption by partnering with business and technology teams. They work to embed models into core workflows, operationalizing data science to deliver sustained business impact.

Key Accountabilities

• DATA PREPARATION MANAGEMENT: Conducts extraction and integration of complex data from different data sources, analyses the ways in which datasets may be biased and applies mitigation strategies.
• DATA ANALYSIS: Reviews complex data sets for exploratory data analysis to identify trends and patterns that inform business strategies across various areas.
• MODEL DEVELOPMENT: Develops and deploys artificial intelligence models, including monitoring and evaluating ongoing performance to solve complex business problems and derive actionable insights.
• AI ENGINEERING: Establishes software and artificial intelligence engineering patterns and principles to design, develop, test, integrate, maintain and troubleshoot complex and varied generative artificial intelligence software solutions and incorporates security practices in newly developed and maintained applications. .
• DOCUMENT & REPORTING: Documents development and code in ways that allow for support and knowledge sharing.
• COMMUNICATION: Presents techniques and results to technical and non-technical audiences.
• CONTINUOUS LEARNING: Examines existing and emerging artificial intelligence and optimization principles, theories, and techniques to develop and deploy artificial intelligence models into production, improving the organization's analytical capabilities.