Data is where it’s going, and where it’s at in the corporate world. Facebook, Google, and Uber have all demonstrated to the world that machine learning can drive real business value. But when Jason Salazer-Adams ’06 graduated with a mathematical and computer science degree from Denison, he didn’t know that he would have a career in data analytics or data science—frankly, there wasn’t a data analytics major to be had at most schools. (Denison didn’t launch its own data analytics major until 2016.) Now, when you search data sciences, there are nearly 1,000 jobs open with the title “data scientist.” But what Salazer—Adams did have was a professor in the math department who was passionate about operations research. “That’s what really sparked my interest in building mathematical models to solve a problem,” he says, “trying to minimize cost and maximize revenue.”
That interest led him to Starbucks, which relies on data analytics to help route its trucks to some 30,000 stores worldwide and figure out how much inventory should be held in a distribution center. Now, as part of the global supply chain strategy and deployment team for Starbucks, Salazer-Adams sees the big data picture. “What we do, in general, is we try and process all the data that Starbucks collects, whether it’s through a customer purchasing a product or the way that we handle product in our distribution centers,” he explains. “Then we try to derive insights from the patterns of those data.”
Salazer-Adams works on the operations side, understanding how well the supply chain is performing and building insights from there. He’s involved in forward—looking decisions, forecasting what is going to happen. For example, his team looks at historical data to see how Starbucks has been doing in terms of providing inventory for stores and then uses that data to place orders.
Salazer-Adams is currently transitioning into the network optimization team, which is focused on mathematical modeling and trying to answer long-term strategic questions. He’ll look at things like, “Should the company open a new distribution center or close an existing one?” Or “Should we change the way we’re moving our coffee beans from one location to the next?” It’s all an effort to keep your wait for that daily jolt as short as possible.