Alumni Corner – Emily Mo
Emily Mo | Linkedin
“MSSP taught me how to identify the problem, communicate it effectively, and then solve it.”
Organization:
IDEO – A global design and innovation company
Position Title:
Data Scientist / Designer
Responsibilities:
I work with multidisciplinary teams, including qualitative design researchers, to understand humans from both the individual and the aggregate point of view, and then turn the corner towards creating novel products, services, and experiences. I’ve worked on digital banking experiences, interactive entertainment interfaces, and even public space design! Our clients have varying levels of familiarity with statistics and data science: at times the client will have an in-house data expert who I can collaborate easily with; at other times, the client will be a skeptic who needs to be talked through why we don’t need to run a 1000+ sample size survey. There are no textbook-perfect problems in consulting! Since this work happens under business constraints, I find myself still using the communication skills I learned at MSSP to help clients feel confident about our work, even as we make tough choices in our research plans or deal with short project timelines.
Favorite Aspect:
The most valuable skill MSSP taught me was how to flexibly communicate the reasoning behind my analyses, and tailor my words to the stakeholder I’m talking to. When I got my Bachelor’s in statistics, I felt like I was lacking the ability to navigate the messiness of using statistics in the real world, which led me to MSSP for its consulting focus. In textbooks, the problem is already outlined for you and you just have to solve it; MSSP taught me how to identify the problem, communicate it effectively, and then solve it.
Advice to Current Students:
One transformative moment for me was when we worked with an industry partner who handed us a hodgepodge of unstructured data of varying utility, and wanted us to create a model to improve their operations. We found out our partners didn’t know what a linear model was, so we took a step back and used parts of their unstructured data to demonstrate how models can only be as useful as the data fed into them. The experience taught me how to advocate for myself as a data analyst, while keeping the client’s best interests in mind.