In her talk “Policy, Translation, Estimation, and Inference: When Big Data Isn’t Enough,” Shauna Sweet ’03 returned to campus to discuss her career experience in applied analytics.
She is currently employed as a data scientist for Nyla Technology Solutions, and her work has taken her all across the world, including countries like Afghanistan and Nigeria, for the Department of Defense.
You can’t automate the decisions that matter—translation, prioritization, understanding bias, critical thinking. If there are no data scientists, then who is going to interpret the data? Who is going to make it mean something?
Sweet describes her career path as anything but linear. “The best way I can put it is that I fell backwards into it, made a bunch of wonderful mistakes and ended up here,” she said.
Graduating with a sociology major, Sweet stumbled into the career of data science almost by accident. “In all honesty, there were very few spots in the career I had been aiming for. I stumbled upon a job description for something called a ‘data scientist’ and thought ‘I can kinda sorta do that,’” she said. “Things just escalated from there. I found my passions and my niche within that field, and now I’m very happy with my career.”
For students pursuing data science as a career, Sweet identified three different approaches for the field of data science: mathematics, computer science, and social sciences. She encourages students not to limit themselves—referring to her own sociology background—and recommends that all hopeful data scientists could benefit from a statistics class.
Sweet warns students that the field is projected to be increasingly automated within the next five years, but she isn’t giving up hope. “All of it can’t be automated. You can’t automate the decisions that matter—translation, prioritization, understanding bias, critical thinking. If there are no data scientists, then who is going to interpret the data? Who is going to make it mean something?”
