[CIO.COM] WINE, WOMEN AND DATA SCIENCE
January 24th, 2017 by Jill Dyche, Vice President SAS Best Practices
Blog post published in CIO.com
From a San Francisco Bay Area perspective it was nothing new: A clutch of hip tech types drifting through an innovation lab sipping merlot, downing sliders and debating hyperpersonalization and the connected car. What was different about the scene was that women made up roughly 95 percent of the crowd.
Co-sponsored by The Hive and Verizon Ventures (which provided the venue, featuring bird’s eye views of the Bay Bridge and the Ferry Building), the Women in Data Science meetup focused less on the term data science — after all, referring to data science as “sexy” has devolved into a Silicon Valley drinking game — and more on the data science toolbox.
The attendees were mostly millennials, and they were impressively savvy about the data science companies creeping into Silicon Valley like fog from the bay. Hummus and veggies and a chardonnay in tow, I listened in on conversations that spanned regression models, Git, identity masking, the pros and cons of Spark, chatbots and the new Nvidia chip. These were heads-down, hard-working and handy grrrlz. Indeed, many of them were self-taught programmers who’d stumbled into data science and intended to stay.
They were also hungry for industry buzz, networking opportunities and career advice. Despite amplified attention on STEM careers and female-led startups, the only thing favoring women at most Bay Area tech events is the lack of restroom queues. The Hive and Verizon Ventures are two of a handful of firms making connections with and between women in tech, providing a forum for news, referrals and future gatherings.
But when it comes to data science, women actually might have an edge. It turns out the “Best Job in America” is also one of the hardest to fill. Companies desperate to hire data scientists are less interested in their candidates’ career pedigrees and educational bona fides, instead targeting the tricky mix of skill sets they need to wrangle, analyze and provision their data. The novelty of the job title and the accompanying tools means that most candidates are on equal footing in the interview process.
And yet. A woman raised her hand during our panel Q&A. “I go back to work after putting my kids to bed,” she shared. “How do I stop feeling so guilty?”
“What a refreshing question from a woman,” I replied. “I usually get it from men!” We all laughed, and then laughed that we were laughing. Clearly women in data science are not only bright, tech-savvy and engaged — they have an appreciation for the absurd.