Data science is a rapidly growing field, but it’s not growing equally. What if I told you that over the past 30 years, the proportion of women in Computer Science has decreased significantly? From 1984 to 2016, the percentage of computer science graduates who were women dropped from 37% to 18%. In short, as Computer Science has become increasingly in-demand, it has actually become less open to women.
Of course, many women are interested in Data Science and the many exciting career opportunities in data. So we need to consider what the statistics of gender in Data Science tell us. What can we do to encourage women to pursue careers as data scientists? And what can current leaders in the field do to support women in their organizations? After all, losing female data scientists means losing a massive chunk of this industry’s potential innovators and leaders.
The gender statistics within Data Science tell an uneven tale. According to a Harnham report, in 2020 just 27% of roles in Data and Analytics were held by women. That average is slightly skewed by the higher number of women in Analytics and Insight and in Life Science Analytics. The gender ratio is lower in Data Science itself: only 20% of data scientists were women. There were, as of 2020, four times as many men as women within the Data Science field. Women also earn less than men. In the field of Data and Analytics as a whole, men earn 17% more than their female counterparts. Some of this discrepancy may be accounted for by a wave of new female data scientists joining the field in entry-level roles. But the pattern is significant: Men are out-earning their female colleagues by a substantial margin.
Although bootcamps and degrees in Data Science are proliferating, women form a low percentage of students in these groups, meaning fewer female graduates to look for Data Science jobs. The underrepresentation of women within tech fields starts at a young age. Until the age of about 11 to 12, girls tend to display interest in STEM fields equally with boys. By the time they’ve reached 16, their reported interest has fallen drastically. This pattern emerges from a broad gendered problem in our culture and across education systems. Teenage girls are less likely than their male peers to see STEM fields as a place for them. They are less likely to be encouraged by teachers, even when they perform well in classes. They may also be discouraged by a lack of female role models in STEM careers.
According to Better Buys, women are twice as likely as men to quit a position in a tech job. As in many fields, women are more likely than men to quit or take time off in order to care for children or elderly family members. Even if they come back to the workforce, they may be at a disadvantage due to being perceived as having missed time. Companies can directly tackle this issue by offering equal paid parental leave to both men and women. More companies around the world are creating equal leave policies. This distributes the work of child care more evenly and, over time, seems to be gradually shifting the cultural stigma against taking a leave for childcare.
But some of this issue is specific to Data Science. The lack of women in the field can become self-perpetuating; women do not see other women in leadership roles and expect to find fewer opportunities for advancement. And a lack of women on tech teams can lead to a potentially toxic environment. In an interview with Master’s in Data Science, Jana Eggers noted that “some sexism and bias problems” were a reason women aren’t always staying in the industry. In the same interview, Lillian Pierson commented that “women constantly face gender discrimination based on how we look and what we wear.” Organizational leaders need to challenge issues of sexist culture head-on. If women aren’t respected, they have no reason to stay.
Even though the unequal gender ratio of Data Science is a challenging problem to solve, it is one that data scientists and educators have been working on for years. The positive result from all of this work is that there are a number of professional networks and resources to support women in tech fields: From teaching girls to code to providing professional mentors to women entering Data Science. There are a lot of helpful resources out there. If you’re a woman in Data Science, or looking to support a young woman in your life, check out the following programs.
Programs for girls and young women:
Resources for women in Data Science and tech:
The field of Data Science is ripe with opportunities for both men, and women. It’s up to people already in the field to ensure that their organizations are not only welcoming, but actively supportive of employees of all genders and races. By supporting women in Data Science, we benefit not only women in the field, but the future innovations of Data Science as a whole.