This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: When Miriam Friedel started working at Capital One in 2020, generative AI was still largely a research curiosity. By the time she left the company as vice president of machine learning engineering in 2026, AI tools were writing code, building applications and reshaping how people work.
We spoke to Friedel, who recently joined the University of Virginia School of Data Science faculty, about the realities of navigating school and work in the age of AI. In this interview, Friedel lays out the AI skills employers are looking for, explains how data science has changed and describes how this technology can be used to augment, but not replace, learning. The hardest part of data science is understanding the right problem to solve.
If you have a massive data set, AI can help you with some of the data exploration and modeling, but it can't tell you if you are solving the right problem in the first place. You need to understand why we are doing what we're doing. What are we trying to accomplish?
How is this going to impact actual humans? Data science also requires a ton of business context, which isn't often available in a form that can be effectively used by AI. What we teach at the School of Data Science is foundational to understanding how AI works.
Loss functions, linear regression, gradient descent—these are the building blocks on the way to understanding transformers, the architecture on which LLMs are built. Ensuring that our students really understand the technical underpinnings of this technology is going to make them more able to use it with both technical rigor and skepticism. Blindly relying on LLMs can be problematic because they're so sycophantic.
They often just tell you what you want to hear, and that's actually really bad for making impactful decisions. You need another human who's going to push back and make sure you are solving the right problem for the right reasons. Generative AI systems produce output based on data they've seen before, and it could be complete garbage.
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