With data becoming the lifeblood of enterprises, fundamental roles like data science have sprung up to get the best out data. But the gender gap in data science is still a problem, even in 2023.

R-Ladies was formed in 2016 to motivate more women to join the data science space and promote gender diversity in the R statistical programming language, according to Gabriela de Queiroz (pictured), principal cloud advocate at Microsoft and R-Ladies founder.

“I was using R back then and I’m like, ‘How about I do something with R; I love R, I’m so passionate about R What about if I create a community around R but not a regular community?’” De Queiroz said. “I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and … be myself and to network and ask questions. I would be in the corner. So that’s how R-Ladies all came together.”

De Queiroz spoke with theCUBE industry analyst Lisa Martin and special co-host Tracy Zhang at the Women in Data Science (WiDS) event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the importance of the R-Ladies organization and the challenges and rewards of being a woman in the data science field.

The importance of a data science community

Since the beauty of data science is that it cuts across different domains, being part of a community plays an instrumental role, especially for women when it comes to navigating this sector, according to De Queiroz. Mentorship is also vital for motivation purposes, she added.

“If you don’t have a community around you, it’s so hard to navigate; you are lonely,” she said. “There is no one that you can bounce ideas off that you can share what you are feeling or that you can learn as well. So when you have a community, you see people like you. Make sure that you have a mentor that can support you through this trajectory, because it’s not easy.”

Based on the diverse application of data science in different areas like climate change and police violence, inspiring others is critical for enhanced penetration into this field, according to De Queiroz, who said that she took up the mantle of motivating other women.

“I joke that I want to be the role model that I never had,” she said. “And once I was tracing my path, I started to see people looking at me like, you inspire me so much. And I’m like, oh wow, this is amazing and I wanna do this over and over and over again. So I want to be that person to inspire others … because that’s so valuable.”

Given that data science doesn’t require a certain background, it presents an ideal stepping stone for those interested in joining the field. Furthermore, it is not constrained to a certain age group, according to De Queiroz.

“Data science such a broad field that it doesn’t require you to come from a specific background,” she noted. “The most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists … started programming when we were nine, that’s not true. You can be 30, 40 shifting careers.”

Want a diverse team? Be intentional

When it comes to creating diverse teams, being intentional is of the essence, according to De Queiroz. This should be the case from writing the job description to the interview process.

“I do love building teams,” she said. “Every time I’m given the task of building teams, I feel the luckiest person in the world, because you have the option to pick different backgrounds and all the diverse set of people that you can find. If you wanted your team to be diverse, you need to be intentional.”

Insights gained from data are playing an instrumental role in transforming different industries. As a result, data science comes in handy when connecting the dots for more sound and information-based choices, according to De Queiroz.

“I was trained in statistics, so I’m a statistician,” she said. “And then I worked in epidemiology; I worked with air pollution and public health. I was a researcher before moving into the industry. The beauty of data science is that you can move across domains. So, I worked in healthcare, financial and then different technology companies.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Women in Data Science (WiDS) event:

Photo: SiliconANGLE