A big differentiator at Helpshift, both for the product and company as a whole, is our approach to implementing artificial intelligence. We actually have an internal team of data scientists building out our machine learning products. Instead of requiring brands to use bolt-on AI solutions, our data scientists are building and testing machine learning algorithms to solve real problems for our customers, 100 percent in house. Today, they are largely working on AI-powered issue classification and real-time trend-spotting.
So how do our brilliant data scientists approach their work?
“Every picture, every image for me is just a bunch of numbers and pixels put together, and I can derive some insight out of that. So if you can give me numbers, if you can give me some sort of abstract representation of data, I can build something out of that,” said Yash Gandhi, Helpshift Head of Data Science, in a recent interview.
Yash spoke with me about both the exciting opportunities and challenges he faces when structuring the data science team, and directing his team to execute on set priorities. In this interview, Yash discusses his background, talks about what it’s like to build a data science team from scratch, and sheds some light on the following topics:
- Assessing applicants in the data science field (his approach definitely surprised me!)
- Using natural language processing vs. other applications of bots and AI to solve narrow problems
- Staying aligned with other teams in engineering and product
Listen here or watch below.