Tay is a senior asset leader in our commercial, trading, and risk analytics work, which comprises a team of former quantitative commodity traders focused on improving clients’ trading performance and AI/machine learning capabilities. Based out of McKinsey’s Denver office, he advises traders, investors, producers, processors, and buyers globally across agriculture, oil and gas, power, metals, and illiquid commodities.
Tay brings 14 years of combined consulting and industry experience in commodity markets. He has helped merchant traders and vertically integrated commodity players stand up and improve data science teams that serve trading, commercial, and value chain optimization functions. He has reviewed over $8 billion in notional exposure for basic-materials players (such as aluminum, steel, and copper), agricultural producers (such as palm oil and sugar), fast-moving consumer goods companies (such as beverages and packaged foods), retailers, and food service operators and franchisors.
His work spans digital commercial transformations, value chain optimization, operating model design, and the deployment of agentic and machine learning models across the trade lifecycle. He frequently leads commercial due diligences for financial investors evaluating commodity processors, equipment manufacturers, infrastructure assets, and technology providers that sell to leading commodity traders.
Examples of Tay’s recent work include:
- standing up the data science function and operating model for a merchant trader of physical commodities, including the development of predictive and what-if scenario models
- building trading analytics platforms and trader capability programs for leading global financial intermediaries and physical commodity traders of crude, refined products, power, agriculture, metals, and illiquid commodities
- assessing capabilities and designing operating models for trading units in agriculture, oil and gas, and metals
- conducting greenfield site selection to identify the most attractive locations to build commodity assets using geospatial analytics of supply, demand, and implied historical margins from physical (basis) prices
- conducting commercial due diligences across commodity trading companies, infrastructure assets, futures and options brokers, and commodity trading analytics software and equipment manufacturers
- leading a trading growth transformation and operating model redesign across crude, intermediates, products, renewable fuels, power, and gas trading desks for a major oil and gas company
