Women technologists who hold leadership roles broke through cultural and structural barriers to get to where they are. Grappling with industry's storied diversity issues, they invented their own recipes for success.
For Michelle Rodriguez, dean of the engineering school at Peru's Universidad del Pacífico, attending conferences often meant wondering why there weren't more women sitting beside her. "You start looking at it, and you think 'something is going wrong here," said Rodriguez, speaking on a panel Monday during Women in Data Science Worldwide.
Nearly four in 10 women who leave careers in tech cite culture issues as the deciding factor, according to a report from Accenture and Girls Who Code. And the percentage of computing occupations held by women is falling, from 36% in 1991 to 26% in 2021, CompTIA data shows.
"When I was young, I [thought] that the technical expertise was enough," said Rodriguez. "I [thought] that if you were good enough, everybody can see it, but when you live in a society that looks at women different, you have to sell, all the time, your ideas."
While seeking positions of leadership, women in data science fostered a mix of planning, communication and professional development skills coupled with their existing technical prowess to help advance their careers.
1. Mastering time management
In the process of earning her PhD at Georgia Tech, Aishwarya Agrawal, assistant professor at the University of Montreal, learned the value of time management for maximizing efficiency.
"For example, how to manage your time, how to plan your different activities on your calendar, and how to keep blocks of time, even for simple simple activities, because each activity takes a lot of time," said Agrawal.
Once a goal has been selected, writing down small small steps toward that goal stands out as a simple strategy for planning, said Agrawal.
"These are the kinds of skills that I learned, either because they were explicitly instructed or just based on group discussions or the environment around me, I saw people having those skills," said Agrawal. "And I sort of started adopting those skills in myself as well."
2. A solutions-oriented mentality
Up-and-coming data scientists need to develop a solutions-oriented mentality, said Daniela Braga, founder and CEO at DefinedCrowd.
"Nobody really learns in school to perform this job," Braga said.
Braga's experience hiring for her company has led her toward common traits of successful technologists: a solid understanding of a specific subject, an active curiosity and an outside-the-box mindset.
Hiring managers look for examples of creativity and resilience while assessing candidates. "The creativity part is the ability to actually come up with solutions, even if it's not necessarily professionally, to problems that are not expected."
3. Upending the mentorship model
Tech businesses have devoted millions to create upskilling and training programs, but mentorship can also help in the face of critical talent shortages.
While mentorship programs can support career development, gaining valuable professional counsel doesn't always require a formal structure, according to Afua Bruce, chief program officer at DataKind.
"You don't need a really in-depth, one-to-one on a weekly basis mentoring relationship with someone," Bruce said. "You can identify traits that matter and that you want to have in your life from just a few encounters, or by following people on social media, or by listening to talks they give."
4. The customer-first approach
Regardless of where data scientists are in their careers, understanding who the customer is can help improve their ability to impact those they serve, Bruce said.
"Your customer may be your manager or decision makers within your company," said Bruce. "You need to figure out how to take the insights that you're finding and package them well, and communicate them in a way that they resonate."
Technologists in more customer-serving roles need to develop a duality: enough emotional intelligence to be able to "read" the client, while having sufficient technical skills to understand their needs, according to Braga.
5. Learning how to sell
Women in the technology space have to contend with structural issues, such as microaggressions during their workday or sexist attitudes from leadership. For women in data science, entering senior roles required exercising soft skills alongside technical prowess.
Communication, persuasion and being able to navigate organizational politics is critical in order to get others to "see the things that you are seeing," said Rodriguez. Navigating organizational culture means not being afraid to show off ideas and projects.