Long before she was working in fintech and AI-enabled decision systems, Nora Yang was fascinated by consumer behavior and the question of why people make the decisions they do. That curiosity eventually led her into analytics, where she discovered that data could reveal stories about people, culture, and business growth.
Today, Yang serves as manager of marketing analytics at Credit Genie, a Series B fintech startup based in New York. In her role, she works across analytics, growth strategy, and data infrastructure, helping translate complex data into business decisions. As AI has become increasingly integrated into analytics workflows, Yang has found herself working at the intersection of data, technology, and strategy.
Before joining Credit Genie, Yang worked at Snap Inc., supporting enterprise advertisers across industries including travel, automotive, and quick-service restaurants. Earlier in her career, she worked in advanced analytics and attribution modeling at Mindshare.
Yang earned her M.S. in Applied Analytics from Columbia University School of Professional Studies, graduating with distinction, after studying business and marketing at New York University.
“At the end of the day,” Yang said, “data without a story is usually just a very expensive spreadsheet.”
Tell us about your career journey since graduating from the program.
I currently work at Credit Genie as the manager of marketing analytics and data platform and analytics. In simple terms, my job is to help the company make smarter growth and marketing decisions using data. I oversee analytics across acquisition, lifecycle marketing, monetization, forecasting, experimentation, and customer growth, including work like LTV modeling, ROAS analysis, cohort analysis, and forecasting future business scenarios. I also partner closely with product, finance, engineering, risk, and executive leadership teams. One of the most exciting projects I’ve worked on recently has been helping architect AI-forward analytics infrastructure internally.
Before this role, I spent several years at Snap Inc., working with some of the platform’s largest advertisers. The Applied Analytics program at Columbia helped me tremendously because it balanced technical skills with business applications. Beyond strengthening my foundation in statistics, SQL, machine learning, and business strategy, the program taught me how to structure problems, communicate insights clearly, and make decisions in situations where there often isn’t one correct answer.
How did the Applied Analytics program help you navigate the transition to a new industry?
I’ve transitioned across several different industries during my career—media, big tech, and now fintech—and every move initially felt like learning a new language. I started at Mindshare working in media analytics and attribution modeling, then moved to Snap Inc., where I focused on enterprise advertisers, experimentation, measurement frameworks, and business strategy. Transitioning into fintech at Credit Genie was probably the biggest learning curve because I had to quickly understand lending dynamics, monetization models, customer risk profiles, and financial metrics.
What helped me navigate those transitions was realizing that analytical thinking itself is highly transferable. The industries changed, but the core process stayed the same: solve business problems using data. That mindset was heavily reinforced during the Applied Analytics program at Columbia University, which trained us to think structurally about business problems rather than memorize one specific industry playbook.
The program also helped me build technical confidence and become more comfortable navigating ambiguity, which made adapting to new industries much less intimidating.
What were some of your favorite courses during the program, and how have they been useful to your career?
My favorite courses were the ones that mirrored real-world business ambiguity instead of relying on textbook exercises. One course I especially enjoyed was Storytelling with Data and AI because it reinforced something that became incredibly important in my career: good analysis means nothing if nobody understands it.
I also enjoyed the Strategy and Analytics course because it connected analytics directly to business decision-making, which later became highly relevant when I began working with leadership on forecasting, growth strategy, and investment planning. Research Design was another course I found extremely useful because experimentation and hypothesis testing became central to my work through A/B testing, incrementality analysis, and causal measurement.
I also valued the Capstone course because it mirrored actual industry dynamics more closely than any textbook exercise. The curriculum required us to navigate ambiguity, collaborate across functions, and deliver actionable recommendations under tight constraints—an experience that essentially serves as a preview of professional life, albeit with fewer Slack notifications. Looking back, I deeply appreciated how the program balanced technical proficiency with strategic thinking. The Applied Analytics program wasn’t training us to be only coders working in isolation; it was shaping analytical problem-solvers who can operate effectively within business environments and communicate complex insights clearly across diverse teams. Maintaining that equilibrium between data and strategy has easily been one of the most significant advantages in my career journey.
What activities did you participate in outside of the classroom?
During the program, I participated in networking events, industry panels, alumni discussions, and Applied Analytics program activities. The peer network itself was one of the most valuable parts of the program. My classmates came from consulting, engineering, finance, health care, marketing, and other fields, and seeing how different people approached problem-solving really expanded my perspective.
Being in New York also created opportunities to attend tech events, startup discussions, and industry talks outside the classroom, which made the experience feel deeply connected to the real world rather than purely academic.
What advice would you give to new students?
Something I wish I knew before starting the program is that nobody comes in knowing everything, even the people who seem very confident on day one. Analytics can feel intimidating at first because there are so many moving pieces, but over time, you realize the goal is not to master everything immediately. The real goal is learning how to think analytically, adapt quickly, and become comfortable solving unfamiliar problems.
Another thing I wish I had understood earlier is how valuable relationships would prove to be. Your classmates, alumni network, and professional connections can have a huge long-term impact on your career.
Finally, I’d encourage students to stay curious and adaptable because the field changes constantly. The program gave me not only technical skills but also the confidence to transition industries, solve complex problems, and continue growing throughout my career.
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.
