Gordon was a 20 year veteran of ConAgra foods where he spent his time overseeing their vast supply chain database warehouse. This gives Gordon advanced knowledge of how to store very large datasets and structure them in such a way to use machine learning to extract recommendations that elevate risk and optimize success. Gordon has lead projects spanning from labor reduction to network optimization including: transportation efficiency, real time management dashboards, safety optimization, root cause analysis, demand forecasting, and customer P&L. Gordon also successfully wrote an optimization engine (a sophisticated stochastic simulated annealing heuristic using an evolutionary strategy and a dynamic population size) that successfully identified the key drivers of cost variances and saved over $2M when multiple analysts (internal and external) had been unsuccessful identifying the root cause. Gordon has a bachelors degree in Physics from St. Lawrence University.