When Robert Huang '23 was in the mechanical engineering master's program at the National Taiwan University of Science and Technology, he participated in a research project that aimed to improve the manufacturing process of electromagnetic steel sheets that Tesla used in its motors. "I really enjoyed the process of designing mathematical and statistical models, and using real data to verify the calculations and improve those models," Huang says. "As a result, I gradually developed my love for the field of data science—and that's why I decided to study it at Lehigh."
After graduating this past May, Huang joined Power Secure, which supplies microgrids to electric utilities and their industrial, institutional, and commercial customers.
"As a supply chain analyst, I must not only have strong knowledge of data science and data analysis but also sufficient understanding of the domain knowledge of the industry," Huang says. "My job often involves designing clear key performance indicator, or KPI, reports, which are models for tracking production progress, as well as forecasting models for improving supply and demand."
To design a good model, Huang needs to communicate frequently with engineers to understand every step of their production line. That's where his background in mechanical engineering is an asset in converting difficult engineering terms into easy-to-understand data.
"Lehigh taught me basic Python and SQL knowledge, but I also learned many machine learning algorithms needed by data scientists, as well as the use of big data processing tools such as Apache PySpark, Apache PyTorch, and Hadoop needed by data engineers," Huang says.
He's also found value in the program's focus on project-based learning, which gave him the opportunity to look at real-world problems from start to finish. "This is the same process I use in my job right now," which involves mining the company's existing data for insight into enhancing efficiency, reducing costs, and making informed decisions throughout its supply chain, he says.
Switching from the manufacturing development and product design of the mechanical engineering field to data science gave Huang a new outlook on his career trajectory.
"I hope that within the next three to five years I can combine my experience as a more senior data engineer with a role in management," he says. "I don't want to limit myself to designing products. I would like to build up my expertise in how to make data-driven decisions, and Lehigh provided me with not only a foundation in data science but the opportunity to start learning those more advanced skills."
