Pauline Wang graduated from the School of Information with a master’s in information and data science in 2020. Prior to the I School, she graduated from Barnard College with a bachelor’s in astrophysics and from Columbia University with a master’s in international affairs. She spent over 15 years in the consulting industry before entering the data science field. Currently, she is a senior manager of the ML Data Triage at IBM Watson Orders.
Why did you choose the I School?
I was primarily drawn to the I School for the quality of its curriculum and teaching staff. As a working parent, I also appreciated the flexibility of the course schedule and the availability of asynchronous materials.
What was your favorite thing about the I School?
In addition to the classes, I enjoyed the faculty members' extensive professional experiences, which brought a lot of practical and applied insights into the classroom. Additionally, the tight-knit and supportive student community fostered a positive and collaborative learning environment.
What was your favorite class?
Applied Machine Learning with Todd Holloway! This course provided an ideal balance of breadth and depth in understanding various machine learning models.
You’re currently a Senior Manager of the ML Data Triage at IBM Watson Orders, what compels you to work in the field of data science and machine learning?
Within IBM Watson Orders, our primary focus is creating voice agents designed to streamline drive-thru order processing for quick-service restaurants. I find satisfaction in harnessing the power of technology to automate fundamental human tasks. I enjoy analyzing data, gaining insight from a wide range of clientele in the food industry, and learning all about how people like their burgers!
How do you utilize the skills and lessons you learned at the I School in this role?
The technical courses at I School laid a robust foundation for comprehending complex data challenges. Through engaging projects and collaborative group work, I developed a proactive approach to independent learning. The extensive student network provided career ideas and also served as a source of skilled data scientists for recruitment purposes.
