The Data Science Diversity Gap: Where Are the Women?

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Data science and related fields, including artificial intelligence, business intelligence, and big data, are seeing tremendous growth. Data is important in numerous industries, from healthcare to transportation, making data scientists a must-have role in most companies. As more technology emerges, even more data can be collected, which only increases the need for experts.

Why Are So Few Women Working in Data Science?

There are a few distinct reasons why women are underrepresented in data science. Marketing, encouragement, and education can fix some of these issues. Time and patience will also go toward bridging the gap. It’s also important to clarify misunderstood data; a lower representation of women in STEM doesn’t mean women are as rare in certain fields, like data analytics. However, they are still outnumbered by men, representing just over a quarter of the workforce in data analytics.

Data Science Is a New Field

Data science is a new concentration in the tech field. Fields like user experience design and web development have been around for a long time, which means there’s been more time for women to break in and show their value.

Lack of Early STEM Education

STEM, which stands for science, technology, engineering and mathematics, is taught in a majority of schools. However, while female students receive the same initial STEM education as male students, they’re not retaining interest in the field. STEM lessons aren’t always taught in engaging ways, which makes it easier for females to lose interest, especially when coupled with the lack of support they get from teachers, parents, and peers.

There’s a widespread misconception that fields like math and science are exclusively for males — that they have a better aptitude for those subjects. Helen Chiang, general manager of Minecraft Franchise, says about growing up, “It wasn’t popular for girls to be smart or interested in challenging subjects within STEM. I went through a period of wondering whether I should pretend to not understand subjects, or dumb myself down so that I would be liked.”

Lack of Mentorship

Mentorship can come from school, home, or both. Females of all ages need support and confidence when it comes to succeeding in STEM-related fields, whether that’s a math class in elementary school or a data science career after college. While moms should encourage their daughters to advance their interest and skill in STEM, dads should provide encouragement too. In a world where men feel like women can’t perform in STEM as well as they can, it’s extremely helpful to be encouraged by males who don’t feel that way.

Reshma Saujani, founder and CEO of Girls Who Code, says, “We have to rethink the way we raise our girls. Boys are pushed to take risks; girls are not. In fact, they feel like they have to be perfect at everything they do; they see getting a ‘B’ in math class as bad. We have to teach girls to be imperfect.”

This directly relates to data science, a field that has a large element of trial and error. Saujani says, “The process of learning how to code is learning how to fail. We need to teach girls that it is all right to sit with that discomfort of not knowing the right answer right away.”

Exclusionary Workplace Culture

Data science fields are still unbalanced, with men holding more jobs than women. This can create a male-dominated, boys’ club culture at work. Whether that’s what a company’s culture is truly like or that’s just the impression women get, the result is the same: fewer women are applying for jobs.

On the business side of the problem, companies can make the data field more inviting to women by putting their female employees front and center. On the personal side, women should be encouraged go after these jobs regardless of apparent stigma — they may find that the problem is in their perception, and that the workplace is actually an inclusive, welcoming environment.

How to Close the Diversity Gap in Data Science

Take heart — this lack of diversity isn’t happening everywhere. There are plenty of businesses that see the value in hiring women and are thrilled to have a female at the helm. For example, Alibaba, a tech company, has a workforce of 40 percent women, and six of its 18 original founders were female. Here’s how to emulate their success and help close the data science diversity gap:

Women Can Continue Their Education

Even the most diverse companies want to hire the best people for the job, which means candidates need the right background. Women who want to land a job in data science or move up in their current job should consider enrolling in a masters program in business analytics or data analytics, especially considering the high level of demand.

When entering the job market for the first time, this type of education can put the graduate ahead of the competition. If the individual is already working in the field, she may be eligible for a promotion after going back to school to continue her education.

Classrooms and Workplaces Should Be Inclusive

No matter how few women are in a data science class or career in comparison to men, the surrounding atmosphere should be one of equality and inclusion. Female opinions should be heard, valued, and acted upon as often as men’s. Women in data science fields should be interviewed and asked to tell their story, whether that’s for a magazine article or an internal newsletter. In high school and college classrooms, there should be posters on the wall that show women in STEM careers.

Companies Should Seek Out Diverse Candidates

Companies that hire data scientists should market themselves to women’s colleges in addition to the schools they usually target. They should also pay close attention to the schools that have a high amount of female tech students, such as the California Institute of Technology. Companies should show up at career fairs for these schools — not only to seek out more diverse future employees, but also to show female students that they’re wanted in the field.

As of right now, women can only look forward to diversity after breaking into the field, showing their value and forcing female representation to become the norm. Unfortunately, that’s the world we live in — underrepresented cultures and races have had to do exactly that for decades. There’s no escaping the historical lack of diversity, and acceptance isn’t the norm yet. Being forced to work harder to show your worth isn’t ideal, but it is better than the past, when women weren’t given any opportunities at all.

Data science is a much more creative field than you think. Diversifying data science teams means opening the door to different, more creative perspectives. This is especially important when the tech that’s being developed will specifically combat women’s issues, such as women’s healthcare. Show your innovative side in order to be of true value to the company you work for. Modern, savvy companies won’t care about your gender if you’re brilliant at what you do.

About the Author

Avery Phillips is a freelance human based out of the beautiful Treasure Valley. She loves all things in nature, especially humans. Leave a comment down below or tweet her @a_taylorian with any questions or comments.

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Speak Your Mind



  1. Jerzy Kaltenberg says

    Instead of another ‘where are the women’ whinge, why not do some actual analytics? Why assume that extra effort to get male-female parity is worth it? Is there data to support this automagical, ideological assertion? do ‘diverse’ enterprises do better? If so, why isn’f the drive fif greater profit translating into more diverse hiring practices?

    Unless women bring more to the table than the additional costs of diverse hiring ( quotas which exclude the best fit on sex or cultural grounds) , “forcing female representation” or additional outreach & marketing, the answer is unlikely to be yes.

    Is it the automatic assumption if female inferiority which drives these efforts to cajole and bully women into doing things they are – statistically – choosing to do in smaller numbers? Why can’t we trust the choices women make when they choose _not_ to major in engineering or other STEM fields?

    Why is it that numbers if women in tech are roughly inversely proportional to their degree of enfranchisement? It seems – and i don’t want to be hasty in my assumptions – that when women get to make more choices about what they do, fewer wind up in tech and more in life sciences and sociology.

  2. Matthew Rocha says

    You can’t write an article urging more women to study data science when you are a women who didn’t study anything stem related. When women aren’t involved with STEM talk about STEM it makes me cringe, get involved and then you can talk. You’re the reason why women don’t get involved in a career in STEM. Sorry for my bad writing and grammar but I’ve been coding all day and actually have a career in STEM.