Meet Chenyi Cai, a Data Analyst at ALTEN China, whose role bridges technical precision and creative communication. He sees himself balancing two core missions: uncovering insights through algorithms and telling meaningful stories through data. On one hand, he works on modeling solutions to optimize fuel efficiency and predict aircraft performance issues. On the other, he translates those findings into clear, actionable advice for clients. This blend of analytical thinking and storytelling doesn’t just define his work—it shapes who he is: detail-oriented, curious, and full of energy.

( It will take you 5 minutes to listen to the audio and 3 minutes to read the paragraphs below. )

Episode Transcript

[00:55] Three Words That Describe Me

Adaptable, responsible, and passionate.

Whether during internships or in my current role, I’ve always been quick to adapt to new environments. For instance, during one internship, I dove straight into core data analysis projects, learning by doing and picking things up quickly. I hold myself to a high standard of accountability—especially when working with global clients. Dealing with time zone differences, I often adjust my schedule to align with theirs, such as responding to urgent tickets at night or coordinating international meetings.

Outside of work, I enjoy cycling and racing—the thrill of battling the wind reignites my passion. I also recently adopted a cat, which has brought a touch of emotion and a deep appreciation for life into my otherwise data-driven world.

[02:02] A Day in My Work Life

My day typically starts with a scan of my inbox and our support platform, like Freshdesk, to catch up on any client requests that came in overnight. These can range from investigating unusual fuel consumption on a specific flight to forecasting maintenance cycles for onboard systems like air conditioning.

Once I understand the request, I dive into building or refining data models, checking the logic behind our calculations, and sometimes collaborating with teammates to troubleshoot data integration issues.

Our team also rotates responsibilities for monitoring system health in real time, making sure client data is flowing correctly and everything is running smoothly. If a client wants to enhance their dashboard—maybe by adjusting how charts display or improving the thresholds for alerts—we team up to implement those updates quickly and effectively.

Whether I’m digging into complex data patterns or fine-tuning the user experience, every task is about delivering insights clients can trust—and act on.

[03:14] What Drew You to Data Analysis?

My background is in civil aviation, but I had limited exposure to Python before starting this role. What attracted me to this position was the opportunity to blend my technical background with data analysis. For example, using flight data to predict aircraft failures made me feel like I was solving real-world problems through data.

After joining, I started learning programming tools from scratch and gradually applied theory to practice—building fuel-saving algorithms, refining safety alert models, and so on. This hands-on growth solidified my career path.

[03:56] How Have You Changed Since Starting This Job?

The biggest shift has been in how I communicate. I used to jump straight to technical conclusions. Now, I focus on explaining things in ways clients can understand—instead of simply listing formulas.

I"ve also become much more data-conscious. I often find myself thinking in terms of data in everyday life—for example, estimating the time differences between commuting routes. It’s not unlike the route optimization we do at work.

[04:44] What Makes a Great Data Analyst?

Surprisingly, it’s not just about strong math skills—it’s about the ability to bridge the gap between technical analysis and real-world needs.

A great data analyst doesn’t just run calculations; they understand what the client is really asking and turn that into a solution that works. For instance, if a client wants to improve fuel efficiency, it’s not just about running a generic model—you need to factor in aircraft type, flight route, weather conditions, and operational constraints, then tailor a recommendation that is both accurate and feasible.

Equally important is having a strong sense of what “makes sense” in the data. If our system reports a 500-kilogram fuel saving on a flight, but historical trends for similar routes show a typical range of 200 to 400, that raises a red flag. We have to know when to dig deeper—maybe it’s a model issue, maybe it’s a data glitch. Either way, good analysts are curious, skeptical, and always ready to investigate further.