When tariffs shift, currencies swing, or climate events disrupt operations, most supply chains scramble to understand the impact. Justine’s causal AI and econometric models enable Auger customers to anticipate these macro shifts, quantify business impact, and execute optimal responses before competitors react.
Justine brings more than 20 years of experience at the intersection of AI, economics, and data science, with a career spanning academia, big tech, and public policy. Before joining Auger, she was Vice President of Science at Amazon, where she built and led a 200+ person interdisciplinary AI and economics team. Her group delivered generative AI tools, predictive models, and causal inference systems that drove significant savings while improving the workforce experience for over 1.5 million employees. She pioneered ethical AI frameworks to ensure fairness and compliance at scale, and launched tools to measure causal impact so leaders could understand what worked — and scale it. She also spearheaded the development of causal AI models to optimize retail offerings by accurately measuring customer demand.
In academia, Justine was a Professor of Economics at Brown and Yale, with research spanning competition policy, regulatory design, labor economics, and consumer behavior. Her work estimating behavioral economics models and their impact on consumer decisions was cited in the 2017 Nobel Prize in Economics. Beyond academia, she founded a tech nonprofit that translated her research into AI-driven recommendation systems to improve public policy outcomes.
At Auger, Justine leads the development of econometric, forecasting, and causal AI/ML models that power our agentic supply chain operating system. By embedding global economic intelligence — from trade policy and regulatory changes to currency swings, climate disruptions, and market volatility — directly into demand forecasting and sourcing optimization, she ensures Auger customers can forecast with confidence, build resilient sourcing strategies, and seize competitive advantage.
