In a recent interview with Wired magazine, Claudia Perlich, Chief Data Scientist at Dstillery, points out a largely overlooked issue – The lack of women in the data science industry. Ms. Perlich says that “One answer is that many women in data science are simply not in the right places to be seen”.
What is this ‘place to be seen’?
It surprises me that after all the innovations and discoveries that has given both genders equal recognition, there is still a stage and a backstage in data science.
The stage here is obviously the platforms where data science and its analytics buddies hang out to create magic and history. As Ms. Perlich puts it, the magic of data science happens at the Googles, Facebooks and startups. And she is right.
Academia is distinctive from industry. Industry is where the theories are put to the test and it is industry experience that college students flock towards. Companies like Google with its Deep Thought and Facebook’s FAIR lab are upstanding citizens in the data science world. Moreover they prove Ms. Perlich’s point; data science is more fun when you get your hands dirty, and not when you read it from a book.
While researching for this article, something that stood out was that there are no women pioneers for data science. There are no Caroline Herschel’s of big data, no Sally Rides of machine learning. Well they do exist, but tucked away in academia.
Academia is an outstanding field. It is after all the cornerstone for any innovation. But it is surprising that beyond academia, most women find it difficult to find a place for them in the data science industry. Academia offers a predictable future – fixed work and the promise of tenure.
The situation is changing slowly though. Many organizations have come up (Girls Who Code for example) that encourage young girls to think and analyze data right from high school level. Corporation like Microsoft sponsors an annual KDD Women of Machine Learning Breakfast, where researchers and engineers from Microsoft meet to provide networking help to women in the industry. Similarly Bosch holds the Broadening Participation in Data Mining (BDPM) workshop to encourage minorities and underrepresented groups in data mining.
It’s these workshops that offer support to women working in the industry to make the move from academia. Academia is all fine and well but at the end of the day, it’s what you contributed to creating that gives a sense of satisfaction. And if that doesn’t work out, hey, there’s always teaching.
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