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Since joining EquipmentShare in October 2023 as the director of fleet optimization, Hayden Mills has been trying to find data-driven solutions to a complex puzzle with constantly shifting pieces. What is the right size, mix and allocation of fleet for a company with billions of dollars worth of equipment spread across hundreds of locations?
Fortunately, Mills has a knack for cutting huge challenges down to size.
“I think my strength is being able to break apart an object — whether that object be a process, data, a product or an operation — into its fundamental parts,” said Mills, who leads a five-person team with expertise in data science, engineering and product development. “Once I understand what those fundamental parts are, we can rebuild and put those back together into a more optimized structure. One of the other strengths people say I have is the ability to put the right person on the right problem, matching skills with opportunities.”
EquipmentShare has always been a combination tech/equipment rental company. The T3™ operating system not only adds value to each machine EquipmentShare rents, it also generates mass quantities of valuable information for data scientists like Mills on the company’s growing Algorithms and Inference teams.
Mills got into the data science business almost by accident. He grew up on a farm in Washington state and had a competitive streak, which applied to sports, schoolwork or pretty much any other activity in which score was kept. He gravitated across the country to one of America’s meccas for overachievers — the U.S. Military Academy at West Point.
After he graduated with a degree in math, Mills’ military career was cut short by a medical retirement, but the leadership skills he developed at the academy were quickly put to use. He took a job at Amazon as an area manager.
“I walked into a distribution center, and they said, ‘OK, go figure it out. Have fun,’” Mills said.
He figured it out so well that within four years he was the senior manager for operations with more than 1,400 people reporting to him.
“I was starting to understand that we can apply mathematical modeling or data science solutions here, here and here,” Mills said. “Hacking together a couple of different Excel and VBA scripts and expanding that to Python later on led to some pretty cool innovations, especially when we were working with robotics in the warehouses. That led to me rekindling my affinity for mathematical applications.”
After a yearlong detour to the University of Washington to earn a master’s degree in applied mathematics, he returned to Amazon for four more years, ultimately as a senior manager running cross-functional teams building data and tech products. He made the jump to EquipmentShare because he heard great things about the people and was eager to tackle the massive challenge of fleet optimization.
He manages far fewer people than he did at Amazon, but that’s allowed him to give each team member the level of attention they need, as well as dive into problems himself.
“If you need some extra support, let’s go, let’s review code, dive into the process, do whatever we need to do together,” Mills said. “On the other side, if I can step back and you can give me updates every week? Heck, yeah. I can be as hands-off as you feel comfortable with, and we can go from there.”
Mills’ cross-functional team has helped him tackle different aspects of the fleet optimization process.
The first step was to meet with EquipmentShare’s corporate fleet team to better understand the current processes and decision-making criteria and build proofs-of-concept to show there was a runway for innovation. Then he and his team started building algorithms and data processes that mimicked current behavior so they could track the results and identify potential improvements, creating a cycle of improvement.
One step at a time, he gets closer to the goal of creating a model that can reliably recommend the right mix of equipment for EquipmentShare as a whole and for individual branches.
“He is a process magician,” said Acting Director of Data Science Danielle Quinn, who frequently collaborates with Mills. “He has a very good mind for connecting all the dots to get a full understanding, which allows him to identify which parts of the process are most appropriate for investment or change. That is his approach to the ambiguity: Let’s understand the process and then pick it apart. He can distill actionable things that are deliverable and meaningful from an ambiguous space.”
