When Yuemeng Nicole Chen (’24SPS, Applied Analytics) graduated from Boston University in 2022 with a bachelor's degree in communications, she wasn’t sure what kind of job she wanted or what industry she should work in. She did know one thing, however, thanks in part to her double minors in computer science and statistics, and an internship she had doing social media for a cosmetics company: dealing with data was inevitable, across all industries and jobs.
“Data is entrenched in all parts of day-to-day work,” Chen said in a recent interview about attending Columbia’s Applied Analytics program and her career trajectory since then. “With an applied analytics degree, you can basically work in any industry that you want.”
Since attaining her M.S. in Applied Analytics from Columbia in 2024, the native of Guangzhou, China, has been able to use her degree at jobs in two very different industries, first as a product analyst at an art investment startup and now as a data scientist at CVS Health, a more traditionally corporate environment.
Was there a particular moment when you first realized that data plays such an important role across industries?
During an internship at a cosmetics company in China, my team spent a lot of time on social media listening and campaign planning, which meant analyzing campaign performance, key metrics, and ways to optimize it. That work sparked my interest in data science, and I later became a TA for a computer science course I loved, a role I held for three years.
What brought you to Columbia’s Applied Analytics program?
After my undergrad, I wanted a master's program that was more flexible and that would allow me the time to explore different career options while living in New York City. Columbia's Applied Analytics program was perfect because it gave me the opportunity to explore before committing to one path. That mattered because when I graduated with my bachelor's degree, I didn't know which industry I wanted to go into, but I knew a degree from Columbia would allow me to enter any of them, since data professionals are needed everywhere.
Can you tell us more about your roles since graduating from Columbia?
After I graduated from Columbia, I joined Masterworks, an art investment fintech startup. The company purchases blue-chip arts, securitizes them with the SEC, and allows individual investors to purchase shares. I was drawn to the intersection of art, finance, and technology, especially since art has always been a personal interest of mine, and I was excited to work in a startup environment.
After nearly two years there, I decided to move into a larger corporate organization, and I now work as a data scientist at CVS Health on the customer engagement side. My team explores and experiments with emerging communication channels, such as RCS (basically branded text messages with more interactive features), to better engage with our customers. What I enjoy about the role is that although CVS Health is a huge company, my team operates with a very innovative and experimental mindset. In many ways, it feels like working in a startup inside a much larger company.
Was there one class in particular that stands out for you during your time at Columbia?
The Anomaly Detection class with Professor Christopher Kuo was especially valuable. We explored how data science methods can be used to identify unusual patterns in large datasets, with applications like detecting credit card fraud or suspicious insurance claims.
What stood out to me was how the course connected more advanced techniques to practical business problems. The homework assignments were based on realistic use cases, so we were really learning how to apply and evaluate these methods in real-world contexts. I think that ties back to one of the reasons I really appreciated this program: it emphasizes practical application alongside academic rigor.
What changes are happening in your field that you think students should be prepared for?
AI is changing the data field really quickly, and even since I graduated, the tools have become much more capable. I think it is important to be open to using them, but also to think carefully about where they actually add value.
A complicated SQL query that used to take an hour might now take five or 10 minutes with AI, which opens room for more complicated problems. Beyond speeding up one task, there's an opportunity to look at the whole workflow: automating repetitive steps and rethinking how the process is structured.
At the same time, AI does not replace the need to understand what you are looking at. You still have to ask the right questions, recognize the limitations of the data, and look at the results from different perspectives. I think being curious, adaptable, creative, and willing to challenge the output instead of taking things at face value is becoming more important. That is something I am still working on in my own role as well.
About the Program
Columbia University’s Master of Science in Applied Analytics prepares students with the practical data and leadership skills to succeed. The program combines in-depth knowledge of data analytics with the leadership, management, and communication principles and tactics necessary to impact decision-making across industries and organizational functions.
Learn more about the program here. The program is available full-time and part-time, online and on-campus.
