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Julie Novic knew at the age of eight that she wanted to be an archeologist when she grew up. Even at that young age, she wanted to understand human culture and behavior on a fundamental level. Today, decades later, Julie is now a Data Scientist at Pandata. We sat down with Julie to learn about her journey from anthropology to AI—and to get her expert insight on trends in the AI industry.
I grew up in a multi-ethnic, multicultural, immigrant household. My father is from Serbia and my mother is from Puerto Rico. My father emigrated to Canada in 1971, then to the United States. My mother and her family left Puerto Rico in the mid-50s, however she still grew up in a Puerto Rican household and community.
So, I was raised amid a mish-mash of culture, and also lived in rural Pennsylvania for a time—resulting in a rural American influence in my upbringing as well. The tension that I experienced between at-home life and life outside of our home made me fascinated with what makes people tick.
Rather than looking at people through a psychological lens, I watched how backgrounds and culture influenced how people thought, behaved and made decisions.
As I grew up, I began wanting to understand cultures and why people are the way they are. So, I attended Kenyon College and earned a bachelor’s degree in anthropology. I then did a masters in anthropology at the State University of New York at Albany, followed by a Ph.D in anthropology at Arizona State University. For my doctoral work, I looked at anthropology through an archeological, economic and social identity lens. I focused on quantitative methods, archaeology and consumer economics.
I eventually realized that I wanted to take an applied approach to anthropology, to understand humans in ways that benefit consumers in a hands-on way. So, I studied data analytics and visualization at Case Western Reserve University.
I was also prompted to look beyond anthropology because the job market for practicing anthropologists is tiny! My experience teaching anthropology made me want to work less in academia and more in applied issues.
For a while I was a success coach at Arizona State. That experience gave me an opportunity to sit in on a data meeting where the team discussed how they apply data to make their coaching and success center more impactful. The data analysts at Arizona State mentored me and answered my questions to help me figure out my path. I eventually moved to Ohio to be closer to my family, and I decided to pursue the data analytics route.
I took part in an amazing data science boot camp through Case Western University because I wanted an in-person experience. The professionals at the camp encouraged me to do meet ups and get involved in the data science community.
I discovered that the data science community is all about who you know and the connections you make. I participated in PyLadies, and met a woman named Marissawho eventually introduced me to another professional, Denise. Denise posted a job announcement for Pandata on LinkedIn. I applied for the position and was hired as a data scientist.
Today I am involved in hands-on technical work, designing and developing human-centered AI. I also help our clients and layfolk better understand the ethical and social implications of AI.
Pandata recently conducted a personality survey with staff—my results showed that I’m much more relationship-oriented than many of my peers are.
As a data scientist with an anthropology background, my work is about understanding the relationships among people, how to create relationships, and understanding motivations and goals from a relationship perspective. I understand relationships between data and the people using the data. My unique perspective is seeing the people behind the data, and the people using it. My perspective allows Pandata to better understand end users and customers, and to help them make better decisions.
Anthropology is much more quantitative than people think because anthropologists deal with counts of objects and measurements. But a “large” dataset in anthropology might only be around 180 data points. By contrast, in data science, a large dataset has millions or even billions of data points. The other difference between the two disciplines is the kinds of questions you ask.
But anthropology and data science are not as unrelated as you might think. Projects like stakeholder analyses, and skills like knowing the right questions to ask, are tied to similar experiences and skills from anthropology.
My primary goals are to create AI solutions that are for people, that are ethical, and that are trustworthy. In other words, I want to create sustainable AI solutions that have people in mind. In data science and AI, it’s not just about automation, it’s also about how we help people through data science.
Explainable AI has become increasingly popular among the AI industry and those that adopt AI within their organizations. It’s not enough to design a black box model that solves a perceived issue—people must trust the data and the AI solution. As AI evolves, more people will (and should) be taking the time to analyze the solution and ask is this really doing what I want it to do?
Julie’s journey is just one of the many unique backgrounds that make up the Pandata team of expert data scientists. If your organization feels ready to design and implement an AI solution that fits your needs, schedule an AI Exploration Session with our team.
In this session, we can discuss your current needs, and dive deeper into ways your organization can implement human-centered AI and machine learning solutions to accelerate your business goals.
Contributor: Nicole Ponstingle McCaffrey, COO & AI Translator at Pandata.
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