Women In STEM: There Is No Room For Stereotypes

Can we make innovation and science gender free?


From MIT to Silicon Valley, the US has long been the top destination for innovation. According to the US Bureau of Labor Statistics, science, technology, engineering, and mathematics (STEM) jobs are growing faster than any other sector. However, what's supposed to be an area powered by forward thinking, the STEM job market is on the list of industries where women professionals are still underrepresented. Female professionals represent only 24% of all STEM jobs in the US.

The tech sector has long been stereotypically labelled as a male territory. The lack of female role models, work/family balance, and gender stereotypes are among the factors contributing to the worsening underrepresentation. These further led to the findings, where according to a report by the US Department of Commerce 'Women in STEM: A Gender Gap to Innovation’ women hold a disproportionally low share of STEM undergraduate degrees and they are less likely to work in a STEM occupation if it's not healthcare or education fields.

Sexism is still present across many industries, but in STEM it's particularly evident. Tech and science fields require a high level of dedication and a specific skillset, but why do some presume that women can't deliver these?

Roots of underrepresentation go back to the early formation of children's minds, where surrounded by their family's views and school environment, youngsters start learning about decision-making and develop their first career aspirations. Centuries of the wrong encouragement among both genders forced many young generations to opt out from their dreams and follow the path of the 'widely acceptable norm.' In this instance, many young boys were encouraged to participate in physical, practical, and logical activities, which led to their advantage in certain fields and disciplines.

Girls, on the other hand, were traditionally encouraged to play dress up and to build their future around family creation, maternity, and activities that don't involve leadership or applied science. However, there has always been a limited number of women in STEM, and this number has been increasing in recent years. 

However, the problem with gender barriers is still far from resolved. People need to change their approach of looking at women – from tech and science's disadvantaged minority to professionals who deserve recognition for their accomplishments and talents, regardless of their gender. Just to give an idea, here are three STEM professionals who lead by example and drive innovation:

Liat Ben-Zur', SVP and Digital Technology Leader at Philips

Liat ensures that connected technology is delivered across Philip's multiple services and projects, including healthcare, consumer goods, and medical devices sectors. With an increasing popularity of the IoT, Liat believes that the future success of the IoT lies in consumer-first approach as opposed to technology. Prior to Philips, Liat's experience includes building Qualcomm's Developer Ecosystem.

Anne Wojcicki, Co-Founder of 23andMe

Biology graduate, Anne co-founded 23andMe, a personal genetics company which enables customers to receive information on health, genetic genealogy, as well as a detailed mapping of ancestral identity. The company led and contributed to dozens of published studies in peer-reviewed scientific journals, which symbolizes their significant value for applied research.

Ann's sisters are Susan Wojcicki, the CEO of YouTube and Janet Wojcicki, anthropologist and epidemiologist at the University of California.

Sinead O'Sullivan, CEO of Fusion Space Technologies

Sinead is an aerospace engineer who developed her interest in space when she was a child. After working at NASA and gaining valuable scientific experience in spacecraft construction, she is now the CEO of Fusion Space Technologies. The company integrates drone and satellite data to create high-resolution mapping with high levels of accuracy. By using this data, they also provide advanced data analytics based on machine learning and AI methods.

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