Gender inequality in the tech industry has gained many headlines of late. For good reason. Diversity is essential to bring new perspectives and help, for example, to reduce the unfair bias of data models, according to Lillian Carrasquillo (pictured), insights manager at Spotify Technology SA.

“Because you are different, your voice is needed even more,” Carrasquillo said. “Your voice matters, and I always ask: How can I highlight your voice more?”

Carrasquillo spoke with Sonia Tagare, host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Women in Data Science conference in Stanford, California. They discussed Spotify’s way of dealing with diversity, and Carrasquillo’s advice for women entering the data science field.

From recruiting to micro decisions about inclusion

Spotify has a team focused on diversity, which encourages all managers to think about inclusiveness, from recruitment to micro decisions that can be routinely made to create an inclusive environment, according to Carrasquillo.

“It’s not just about diversity. It’s also about making people feel like this is where they should be,” she stated.

With the increasing use of machine learning and artificial intelligence, diversity, both of individuals and of educational background, is essential for the search for fairer models, according Carrasquillo.

“I think that a strong, collaborative, and even on an individual level across disciplinary education is really the only way that we’re going to be able to make connections to understand what kind of second-order effects we’re having based on the decisions of parameters for a model,” Carrasquillo explained.

And she knows what she’s talking about. With a diverse education — she has a degree in industrial mathematics and went to a liberal arts college “on purpose” — Carrasquillo leads people with different experiences on her team, which is responsible for thinking about data and algorithms that help power the larger personalization experiences across Spotify.

“I personally manage a data scientist and a user researcher, and the three of us collaborate highly together across our disciplines,” she pointed out.

For women who are leaving college now and going into data science, her advice is that they follow their interests. Because there are many different types of technology problems to solve, women do not just need to just seek a data scientist title, Carrasquillo added.

“You can follow your interest and use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of expertise coming together to work on problems,”she concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Women in Data Science conference.

Photo: SiliconANGLE