To celebrate International Women’s Day, we organized and participated in debates regarding the importance of adding more diversity to technology and Data Science teams, as well addressing challenges for women in the labor market. We invited the co-founder of Mujeres Tech, Cristina Aranda, to talk about the challenges facing women in science and technology.
One of the recurrent issues in the debate was the importance of educating young girls to follow their scientific vocations. It is vital that women understand that they can make a difference in science, and thrive and transform the world through STEAM disciplines. In this sense, role-models are important, as well as a progressive cultural change towards greater diversity.
According to a 2017 report published by the Spanish Ministry of Science and Technology, in 2017, only 25% of all Engineering and Architecture graduates were women. At the same time, a greater participation of women in social science and humanities does not translate into a similar female representation in senior academic roles, such as tenured professors.
Despite improvements in the workplace in terms of equal opportunities, men still hold the lion’s share of leadership positions. In addition, an unconscious bias when hiring women, together with an aggressive and competitive environment make it even more difficult for women to lead the way and assert themselves professionally.
As expected, the debate drifted towards a more analytical approach. Some argued, that a purely scientific approach to hiring should take into account approaches such as counterfactual analysis. If you have two candidates -a man and a woman- with the same experience and skills, one should hire the woman. This is because it is likely that the female candidate will have had to work harder than the male candidate to get to this particular stage in their careers.
On Thursday, two BBVA colleagues, Elena Alfaro, Global Head of Data and Open Innovation, and Ana Laguna, a senior data scientist at BBVA Data & Analytics, participated in the Madrid edition of Women in Data Science, organized by Stanford University. They showed how women are becoming examples of leadership in technology and artificial intelligence development. As a result, they inspired dozens of young students who will be the next generation of women in engineering and data science.