Jason Baldridge has paved a unique path to become co-founder of People Pattern, an audience intelligence company located on the east side of Austin, Texas.
In this edition of “Gigs,” he tells us how being a linguistics professor at the University of Texas prepared him for the big data world and offers insight into what it takes to be the technical founder of a startup.
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Since he was an undergraduate, Baldridge has walked the line between liberal arts and science. He received his Bachelor’s Degree in mechanical engineering with a minor in anthropology. His anthropology studies led him to linguistics.
He studied the underlying algebra of human languages on his way to his Doctorate Degree in linguistics. He later became a linguistics professor at the University of Texas where he was introduced to semi-supervised machine learning algorithms, giving him his first taste of analytics programming.
On his way to tenure, Baldridge started looking into sentiment analysis and automated spelling correction, he says the abundance of data excited him and led him to look outside academia for new challenges.
He co-founded People Pattern with business partner Ken Cho in early 2014. The company ties unstructured social and mobile data with structured client data to make profiles of customers for large companies like AT&T, McDonald’s and Nintendo.
According to Baldridge, launching a startup is not always as glamorous as people think. After the initial excitement, it can become a “slog,” but is important not to lose sight of the big picture.
He says, “You have to have somebody on board with you or you have to have that magical ability to always be worried about the bottom line.”
Another piece of advice Baldridge gives is to pay attention to your industry so you can sell solutions that are in demand. He warns technical founders not to get caught up in the excitement of the technology, but to make sure that people want to buy it first.
As for those looking to get into the data science field, he doesn’t care about a resume or the grades, all he cares about is your code. He advises aspiring data scientist to get experience working on open-source projects involving natural language processing or machine learning and post their work on GitHub.
He says the most important thing to him is how you think and solve problems. Startups often don’t have the resources for extensive training, so he says, “We have to have people that are ready to join and be ready to go.”
For an inside look at People Pattern, check out “Digs” on RCRtv.
