Panel: Diversity in Data Science (Continued)

Women in Data Science: From Inclusion to Influence

Women’s History Month and International Women’s Day are occasions to celebrate the remarkable progress made by women, inviting us to #EmbraceEquity. The inclusion of women in corporate boards, roles in running major companies, and being regularly featured on marketing campaigns as prominent leaders and trailblazers would have been unthinkable as a reality even a half-century ago.

But the truth is, women at the highest levels of business are still rare and statistics also suggest that as women approach the top of the corporate ladder, many jump, or are held off. Overt discrimination is less obvious now, but most of the barriers that persist today are insidious and so deeply embedded in organizational life as to be virtually indiscernible. Even the women who feel its impact are often hard-pressed to know what hit them. 

As women break into becoming a power of influence, discrimination against them still lingers in a plethora of work practices and cultural norms that only appear unbiased. Specifically, in areas such as Data Science, research shows women face unique challenges when pursuing, accessing, preparing, and reaching the top of a career in this field. The stereotypes about women’s abilities start early which undermines their confidence from a young age. A field where they are underrepresented and unsupported translates to a lack of mentorship, empowerment, and advocacy in a variety of areas - resulting in hindering the potential for positive influence.

In this panel, we will look at ways in which these challenges build up to reinforce gender stereotypes, perpetuate inflexible and exclusionary work cultures, and leave women unsupported with fewer role models to influence change. It is time to cross that bridge and for new metaphors to capture the deeply woven status quo of discrimination that still lingers. It’s not the ceiling that’s holding women back; it’s the entire architecture of the organizations in which we work as the barriers to inclusion and influence are not just above women, they are all around them. But dismantling our organizations isn’t the solution. We must strip out the barriers to equity one by one. Leaders must act as intentional architects, rebuild and continue to influence practices that are stronger and more equitable, not just for women in data science but for all.

Join us for a discussion on a subtle pattern of systemic disadvantage that can be hard to notice and therefore missed to be questioned, blocking all but a few women from enacting influence.