Predictive shelf-life modeling for food products

Kraft Heinz is a leading food & beverage company committed to food safety, quality, and innovation. Rapid advancement of data analytics, machine learning (ML), and artificial intelligence (AI) technologies are poised to transform the way companies leverage data to drive new insights in the food industry. As part of our open innovation efforts, we are seeking external expertise to enhance our capabilities in predictive shelf-life modeling for packaged foods. Our goal is to improve the accuracy and efficiency of shelf-life prediction and testing new formulas, ensuring the highest quality and safety of our products.

Seeking: 

We are looking for experts with experience in developing and applying predictive modeling and analytics for shelf-life estimation, particularly in the context of packaged food products. Product categories of interest include sauces & condiments, beverages, powdered mixes, dessert, meal kits, cheese, and meat. The ideal expert will have a deep understanding of the data requirements and advanced modeling techniques, including machine learning and AI, to accurately predict shelf life grounded in food science, microbiology, sensory, and packaging fundamentals.

Topics of Interest:

• Case studies or examples of successful implementation of predictive shelf-life modeling in the food industry
• Data requirements for predictive shelf-life modeling (e.g., storage conditions, formulation, packaging, sensory, etc.)
• Integration of predictive modeling with existing shelf-life testing protocols
• Advanced data modeling techniques, including machine learning algorithms (e.g., regression, neural networks, decision trees, etc.)
• Applications of predictive shelf-life modeling for organoleptic shelf life (e.g., texture, flavor, color, aroma) and/or food safety shelf life (e.g., microbial growth, contamination)

Required Qualifications:

• Advanced degree (MS or Ph.D.) in a relevant field (e.g., food science, statistics, computer science, engineering)
• Proven experience in developing and applying predictive models for shelf-life prediction
• Strong understanding of machine learning algorithms and data modeling and analytic techniques

Please note: Faculty and researchers interested in applying for these opportunities based on technologies developed or disclosed at Vanderbilt must submit their proposals through the CTTC.