Data Scientist

fairlife, LLC is a Chicago-based nutrition company that creates great-tasting, nutrition-rich and dairy products to nourish consumers.

With over $3B in annual retail sales, fairlife’s portfolio of delicious, lactose-free, real dairy products includes: fairlife® ultra-filtered milk; Core Power® High Protein Shakes, a sports nutrition drink to support post-workout recovery; fairlife® nutrition plan™, a nutrition shake to support the journey to better health.

A wholly owned subsidiary of The Coca-Cola company, fairlife, LLC has been recognized by both Fast Company and Nielsen for its industry leading innovation.

To learn more about fairlife and its complete line of products, please visit fairlife.com.

job purpose: The Data Scientist will play a key role in advancing fairlife's vision of establishing a world-class Decision Intelligence ecosystem. This role entails supporting the design, development, and delivery of AI and ML models including production optimization, quality control and anomaly detection, predictive maintenance, supply chain forecasting, and real-time operational analytics. The role will drive innovation and excellence in data science capability, empowering fairlife to leverage data-driven insights for enhanced decision-making and operational efficiency. Increasingly, this work also includes applying large language models (LLMs) and AI agents to automate workflows and augment traditional models where they add value. 

responsibilities:  

• Collaborate with the Senior Data Scientist to design, develop, and deploy AI and ML models supporting analytics and business objectives  
• Engineer and maintain scalable data pipelines for AI/ML workflows  
• Enable AI adoption by developing reusable components, APIs, and automation scripts  
• Support model lifecycle management, including monitoring, retraining, and performance optimization  
• Implement MLOps best practices for version control, CI/CD, and automated deployment  
• Assist in the development of advanced analytics dashboards and tools to communicate model results  
• Apply prompt engineering, retrieval-augmented generation (RAG), and fine-tuning to build effective large language model (LLM) solutions  
• Build and integrate LLM-powered agents to automate multi-step tasks and workflows that complement traditional ML models 
• Ensure responsible AI practices, including fairness, transparency, and compliance  
• Gather, clean, and analyze large and complex datasets to support AI initiatives