After moving from China to the United States to pursue undergraduate studies in applied mathematics at Indiana University, Barry Feng (’22SPS, Applied Analytics) explored several sectors of the financial industry, including venture capital, private equity, and investment banking. Through these experiences, he noticed that much of the work was repetitive and susceptible to automation. This realization prompted him to rethink the skill set he wanted to develop—one that combines analytical thinking, technical capability, and strong communication and collaboration skills.
Columbia’s M.S. in Applied Analytics (APAN) program immediately stood out to him.
“The curriculum offered a unique blend of data, technology, and business applications that matched exactly what I was looking for,” Feng said. “Joining the APAN program was one of the best decisions I’ve made. It equipped me with the ability to leverage modern technologies to solve real-world problems and deepened my understanding of how analytics drives decision-making.”
In a recent interview with SPS, Feng discussed how the APAN program prepared him for his current role, his favorite course, and the industry’s shift from a data-driven era to an AI-driven one.
Tell us about your current role at UBS.
I currently work as an AI model developer at UBS, where I apply advanced analytics and machine learning techniques to improve operational efficiency and scalability.
A key part of my role involves identifying opportunities to automate repetitive and resource-intensive processes using AI. For example, large financial institutions operate across multiple data layers, and maintaining data quality can be time-consuming and costly. Traditionally, this work relies on manual “four-eye checks” or rule-based validation systems. While these approaches provide some level of control, they often lack scalability, adaptability, and predictive capabilities.
In my work, I first analyze the business requirements and underlying data structure. I then design AI-driven solutions that automate validation tasks, improve anomaly detection, and strengthen data quality monitoring. This allows subject-matter experts to focus on higher-value decision-making rather than manual verification.
How have you applied what you learned in the APAN program to your career?
The APAN program played a critical role in shaping how I approach problems in my current role. From both technical and practical perspectives, APAN’s curriculum emphasizes real-world application. Many of the courses were structured around solving practical business problems using data, which directly mirrors the kind of work I do today. For example, in my current role, I often need to translate ambiguous business requirements into scalable analytical or AI-driven solutions. The program trained me to think in a structured way—bridging data, technology, and business needs.
What was your favorite experience or memory from the APAN program?
One of my favorite experiences in the APAN program was a foundational course, SQL.
What made this experience meaningful was that it fundamentally changed how I think about data and its role in business. While the course covered SQL as a technical skill, its real value went far beyond learning a programming language. It taught me how to structure data, think in terms of relationships, and translate technical concepts into actionable business insights.
My professor also played a significant role in making the experience impactful. He had a unique ability to make complex concepts intuitive and engaging, and more importantly, to spark genuine curiosity in students. His teaching style encouraged us not just to learn but to think more deeply about why things work the way they do.
Is there a specific issue, challenge, or topic (for example, AI) in the field of analytics that you think is particularly relevant to the future of the industry? How can current students prepare for it?
In my view, one of the most important challenges for the future of analytics is not simply how to build models but how to responsibly and effectively integrate AI into real-world systems. This includes questions about scalability, interpretability, governance, and trust, especially in regulated industries such as finance.
For current students, preparation should go beyond learning specific tools or algorithms. I would suggest focusing on three key areas:
First, understand AI: develop a strong foundation in the concepts behind modern systems such as large language models.
Second, use AI: gain experience applying AI tools to real problems through projects, internships, or experimentation.
Third, manage AI: learn how to evaluate, govern, and communicate AI-driven solutions, including understanding their limitations and risks.
While it is impossible to predict exactly how the next decade will unfold, those who can adapt, think critically, and connect technology with real-world applications will be best positioned to succeed.
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.
