Lawyers can spend hours combing through dense case law, searching for the right precedent among thousands of pages of legal text. The cost of that time is high, often passed on to clients who may ultimately be priced out of legal counsel.
For Connor Clark (B.A. ‘17), that represents exactly the kind of problem data science and AI are well-positioned to help solve. Now a product data scientist at Harvey, Clark works on AI tools designed specifically for legal professionals. The platform helps lawyers streamline research and document review, freeing up time for the complex, high-stakes work that defines the profession.
“I think legal is likely to be affected by AI in the same way medicine probably will be,” Clark said. “Lawyers spend a lot of time doing very tedious tasks. If we can make that better, then they’ll be able to focus on the things that are more important like strategy and judgment.”
But for Clark, legal AI is only the latest application of a much broader interest: solving problems through data.
Since graduating from UC Berkeley with a degree in data science, Clark has applied his analytics skillset across industries that, at first glance, appear to have little in common, including digital sports entertainment, security technology, and legal AI.
The industries changed, the underlying work did not.
“At the end of the day, a lot of it is still problem solving,” Clark said. “The product decisions are very different, but the underlying skill set of how to solve the problem, how to do a deep dive analysis, how to run an experiment, is similar.”
That adaptability is part of what first drew him to data science at Berkeley.
“What excited me was how applicable it felt,” he said. “You could see how it actually affects the real world.”
Today, Clark works closely with product managers, engineers, researchers, and lawyers to help shape Harvey’s product strategy. Through identifying patterns in how users interact with the platform and designing experiments to test new features, he can help teams decide what to build next.
“One of the things I like most about analytics is that you get to work with a lot of different people and solve a lot of different problems,” he said. “You’re kind of a strategic partner across the company.”
That collaborative approach has also shaped the way Clark thinks about AI’s role in the future of work. While public conversations around AI often focus on disruption and uncertainty, Clark sees enormous opportunity in the technology’s ability to help people learn new domains more quickly. At Harvey, for example, he did not need to arrive with a legal background. Instead, he learned the industry as he worked, using AI tools to quickly build context and understand unfamiliar concepts.
“If I have a question about law, I just ask Harvey,” he said. “It’s the fastest way to get oriented on a new legal domain, which means by the time I’m in a meeting with lawyers, I’m asking better questions instead of basic ones.”
Clark believes the ability to adapt across fields will only become more important as AI tools continue to evolve. But he also emphasizes that strong fundamentals are more critical than ever.
“There’s kind of an easy shortcut now,” Clark said. “But if you always take the shortcut and you don’t know how to go the long way around, then you don’t know if the shortcut is right or not.”
For students entering the workforce, especially in a rapidly changing AI landscape, Clark encourages curiosity and exploration.
“The biggest thing is not being afraid to try something new,” he said. “Right now, people are scared about what the future holds, but I think it’s actually a time to be excited.”
That mindset has guided his own career. Rather than staying within a single domain, Clark has sought opportunities to apply analytical thinking to challenges in a variety of industries.
“The people who are always trying to figure out the next problem to solve,” he said, “are the ones who are going to stay ahead.”
For Data Science Undergraduate Studies inquiries:
brandilyn@berkeley.edu
