Tagged recently hosted a Women in Data Science meetup featuring several lightning tech talks and a panel discussion. As a data scientist on the relevance team at Tagged, I had the pleasure of speaking on the panel with other influential women in this field.
I joined Vivienne Ming, Chief Data Scientist at Gild, and Debora Donato, Principal Data Scientist at StumbleUpon, on the panel. First, we talked about the qualifications of a data scientist. While data scientists are expected to possess certain technical skills (coding competence in Python/R, proficiency with SQL, familiarity with machine learning algorithms and basic statistics), often more important are general problem-solving skills, persistent intellectual curiosity and the ability and eagerness to learn new things. Furthermore, as data scientists often work closely with product and engineering teams, we need to be able to explain our work to several different audiences, and as such great communication skills are a must.
From left to right: Vivienne Ming, me, Debora Donato and panel moderator Sanghamitra Deb.
The second part of the discussion focused on how women can help shape data science. One challenge is getting more women into the field, which is closely related to encouraging women to pursue careers in tech in general. In my opinion, the industry needs to partner with universities and perhaps even secondary schools to help fix the so-called “leaky pipeline,” where the STEM (science, technology, engineering and math) fields lose more females than males at every rung of the educational ladder.
Another topic of discussion was what women currently in data science can do to help shape the general public’s image of data scientists. We should be present and vocal, attend meetups, write blog posts and do whatever else we can to share our knowledge and experiences.
The evening concluded with some questions from the audience. One man asked what he can do to create a welcoming and conducive environment for females as a male on a data science team. Vivienne gave a great answer: the predisposition to treat the minority differently in any environment can be subconscious, so one should engage in careful introspection to watch out for such inclinations.
It is always wonderful to get together with other women in tech and particularly in data science. As someone who has spent so much time in male-dominated fields, I have gotten so used to having almost exclusively male colleagues that I rarely think about the causes and consequences of the lack of women in my space. This meetup pushed me to think about and discuss these issues for the first time in many years.