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What’s Keeping Women Out of Data Science?

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While companies are striving to expand and elevate their data science teams—a task that will become increasingly difficult as demand for data science talent outstrips supply. Despite this challenge, a recent white paper by management consulting firm AT Kearney shows that gender disparity in the field of data science, as females are implicitly (and often explicitly) steered away from data science careers.

This vast structural constraint significantly impedes efforts to scale enterprise data science programs to the next level, notes the report authors.

They also find many algorithms are shown to be compromised by male bias. As most data scientists are men, male thinking inevitably shapes the systems they create. The irony however is, the skills required to be a successful data scientist – critical thinking, structured approach, creativity, intuition, and big picture business view, are all gender-neutral.

Though there are some exceptional women who’ve reached the pinnacles of the profession such as Emily Glassberg Sands, head of data science at Coursera; Yael Garten, director of Siri Analytics at Apple; and Cassie Kozyrkov, chief data scientist at Google, the larger truth remains: data science offers shockingly few female role models.

Deep-rooted bias stems gender imbalance

The whitepaper examines that children are conditioned to think of certain traits as being masculine or feminine. Girls and boys are expected to play different games and to pursue different interests and hobbies. Gender stereotypes are reinforced at home, in school, and through the media, as the “techie culture” narrative is male-centric. Not having an early introduction to computer skills put girls at a disadvantage.

Ingrained cultural predispositions clearly alter women’s choices as they chart their paths through university and career. While up to 74% of middle school girls express interest in STEM (science, technology, engineering, and mathematics)-related topics, only 0.4% of high school girls end up pursuing STEM degrees, suggesting these are the years when women interested in STEM feel most deterred. The proportion of females in STEM studies is lowest in South and West Asia at almost 19%, while Central Asia has the highest proportion at 47%, followed by South America and the Caribbean at 44%.

Moreover, women who do enroll in university STEM programs often face a lack of encouragement and may be made to feel intrinsically inferior in a male-dominated academic arena. At the same time, women who persevere to enter STEM careers may subsequently find themselves viewed as “masculine” and when they negotiate for higher salaries, prioritize career over having children, or make other choices that are often not only tolerated but also expected from their male peers. By comparison, women are implicitly expected to make “softer” personal and professional choices.

This ancient bias carries a modern-day price. A study by HBR reveals that women with eight years’ coding experience show the same level of confidence as men having just one year’s experience. A study using Implicit Association Tests (IATs) across 3,618 participants (male and female) from 78 countries found that five out of six traits associated with brilliance were attributed to men more than women.

Engaging women in data science

The whitepaper recommends helping girl students to pursue their areas of interest and urges educational institutions to offer gender bias awareness programs to parents and educators to help them understand cultural conditioning from a more conscious and objective perspective.

Corporations can coordinate with educational institutions to paint a much fuller, more textured, and more authentic image of data science. For example, they can highlight the varied career paths available within data science (such as data science consultant, data engineer, data analyst), while shedding light on the diverse experiences each path might offer. Most large organizations have women inclusion groups. Engaging female students through such channels would bring them into the business world before they graduate, create mentorship opportunities, and open more tangible paths into a data science career.

A logical extension of this thinking is to actively expand internship opportunities, particularly for female students. Internships help students to directly experience the integral business value of data science instead of viewing it abstractly as a stand-alone technical field. They also allow organizations to get an early peek into talent they may want to recruit.

Data Science: A game changer for women

Companies prefer candidates having the basic technical skills needed for the job, which is customarily evidenced by a degree in data science or some other STEM discipline. However, this filtering process drastically reduces the scope of potential recruits, particularly the already limited number of female applicants, the report noted.

As Ramyani Basu, Kearney partner and UK lead for the Digital Transformation Practice mentions, “I believe in disproportionately pushing for women. I urge hiring managers to find women who fit the role. They’re out there.” She adds, “We need to work harder to recruit them.”

more women are required to play a much bigger part in shaping the future of the  data science industry which is slated to be $140.9 billion by 2024, just as the whitepaper suggests, “Data science needs women’s skills, insights, and perspectives. And data science careers often allow for flexibility in working style, which should appeal to women at all stages of life, including those who want to split their focus equally between home and profession.”

So, while the obstacles are deep-rooted, they are far from insurmountable. The rising popularity and immeasurable importance of data science can change the rules of the game. The results will benefit not only women, but all of data science, and society at large.

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