On any given day, thousands of travelers stare up at glowing departure boards, silently willing one word not to appear: delayed.
It’s a small word with outsized consequences — missed connections, reshuffled crews, frustrated gate agents and ripple effects that cascade across a tightly choreographed global system. In aviation, minutes matter. And behind the scenes, long before a passenger refreshes an airline app in mild panic, someone is working to make sure that delay never happens.
ASU Alum Bhavya Pandya is one of those people.
Originally from Ahmedabad, Gujarat, India, Pandya now lives in Las Vegas, Nevada, where he works remotely as a data scientist employed by GRAero assigned to Envoy Air, an American Airlines Group company. In 2025, he graduated from the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at Arizona State University, with a master’s degree in data science.
Today, he helps transform the aviation industry’s torrents of operational data into foresight that keeps planes moving and passengers on schedule.
“The master’s in data science program at ASU gave me a strong foundation in analytical thinking and real-world problem solving,” Pandya says. “Through rigorous coursework and applied projects, I gained experience working with large, complex datasets, building scalable analytics solutions, and translating technical insights into operational decisions.”
From data to departure
In aviation, understanding the data in time is often what prevents small issues from becoming major disruptions.
Pandya’s work sits at the intersection of aircraft mechanics and machine learning. Airlines generate enormous volumes of data every day — sensor readings from aircraft systems, maintenance logs, flight schedules, turnaround metrics and crew assignments. It can be noise unless you know how to listen.
At Envoy Air, Pandya builds analytics dashboards and predictive models that help teams understand how aircraft are performing, anticipate maintenance needs and fine-tune operational planning. His models help teams see warning signs earlier and help determine how resources should be allocated or where bottlenecks might form.
For travelers, the impact is subtle but significant. Fewer last-minute aircraft swaps. Fewer surprise maintenance holds. More flights that leave when they’re supposed to.
The shift from being reactive to predictive mirrors a broader evolution in data science itself. Pandya sees it firsthand.
“What excites me most is how data science is becoming deeply embedded in operational decision-making, not just reporting,” he says. “In industries like aviation, data has the potential to proactively improve safety, efficiency and customer experience. I’m excited to contribute by building systems that help organizations move from reactive decisions to predictive and preventive ones.”
That preparation began at ASU. The master’s degree program in data science, analytics and engineering trains students to work with complex, high-volume data, but it also emphasizes something less flashy and arguably more important: how to think.
For Pandya, that proved critical.
“Beyond technical tools, the program emphasized structured problem-solving, documentation, and communicating insights clearly to non-technical stakeholders — skills that are essential in a large, cross-functional organization,” he says.
Those habits, he says, mirror the realities of aviation, where data scientists don’t work in isolation. They work alongside engineers, operations teams and decision-makers, and the value of an insight depends on whether others can act on it.
Creating careers that take off
Rong Pan, a Fulton Schools professor of industrial engineering who led the development of the data science master’s degree, says Pandya’s trajectory is exactly what the program is built to support.
“Bhavya’s success at Envoy Air reflects the core mission of our program,” Pan says. “We prepare students not just to analyze data, but to design scalable, decision-driven systems that operate in complex, high-stakes environments.”
In a competitive job market, that kind of preparation matters. His advice for aspiring data scientists who hope to land in global organizations is pragmatic.
“The job market can be challenging, but strong fundamentals, adaptability and persistence go a long way,” he says. “I’d encourage students to focus on learning deeply, building practical experience and staying patient. Long-term growth matters more than short-term pressure.”
Back at the airport, the departure board flickers. A flight that might have been delayed pushes back on time instead. Passengers buckle in, unaware of the predictive models quietly shaping their itinerary.
For Pandya, that invisibility is part of the reward. The best data science doesn’t announce itself. It simply works.
And as aviation becomes ever more data-driven, Pandya plans to keep building systems that make travel smoother, safer and smarter, aiming to keep “delayed” off the screen for more travelers.
