I have to admit that I never really gave the number of women in data science much thought until recently. Maybe it was because, by some lucky accident, my NYU faculty advisor’s two other PhD students also happened to be female. And about half of my predictive modeling group peers at IBM Research were female. And half of the PhDs here at Dstillery are female.
Or maybe because in the bigger picture, having spent the better part of 15 years in computer science departments, research labs, and weight rooms, being around mostly men seemed perfectly normal—in fact, expected. I didn’t stop to consider the “why.”
Claudia Perlich
Claudia Perlich is one of the nation’s top data scientists. She also happens to be a woman, an East German American, a mother, a professor, and a Dressage rider.
But then about one year ago I was asked to be the general co-chair of one of the biggest and most well-established data science conferences in the world: SIGKDD 2014 (KDD). I was the only woman on the committee. It became clear that the decision to select me was in no small part driven by my being a woman. On the one hand, I was pleased that meant that some men take the matter of gender equality seriously, but on the other, I felt cheated because I can never be sure whether I truly earned it.
In general, I find that playing the gender card is not fair to either men or women.
This issue arose as names for keynote and invited speakers were brought forth by the (all-male) committee; no woman was mentioned. Not a single woman was being asked to speak at our biggest industry conference. Not one. Suddenly, I was thrust into the role of having to argue that, “we need to get more women speakers.” That’s a role that I don’t covet. In general, I find that playing the gender card is not fair to either men or women.
How My Gender Plays Into My Professional Life
As a professional, and as a human, I don’t define my identity around being a female. I am a woman; that is a part of who I am, but just a part. My general observation is that being a woman has likely helped my career more than it has hurt it. I have rarely met anybody who made me feel that my qualifications were in doubt because of my gender. I’ve never sought out female role models in data science, nor bemoaned their scarcity, as I never worried for a moment that my gender should limit my ability or accomplishments.
In fact, there is one potentially huge upside to being a woman playing a man’s game: people remember you. You stand out in a crowd that is typically 90 percent men. I go to conferences and many men seem to know me, even when I cannot recall their faces, let alone names. The reality is that we are memorable, and being remembered is very helpful.
How do we make a mark for our ability and not just for the fact that we are female?
But being memorable can be a double-edged sword. A not-so-recent gender study showed that when male subjects are watching the news, they can very well recall the good-looking female presenter, but they cannot recall anything she said. When it comes to the attractive male presenter, however, they may not recall the color of his tie, but they do recall recent developments in the conflict in the Middle East. It’s not that they truly believe that the female presenter is less qualified reading a piece of news, but simply that biology is against us—the subconscious gets in the way.
So here is the conundrum faced by women in male dominated-businesses: How do we make a mark for our ability and not just for the fact that we are female? And how do we make sure that we are called to the stage for the substance of our thinking rather than our gender?
One simple answer it to have more brilliant women in visible positions, such as keynote speaker at a large conference.
The author (center) speaking at the Enterprise Tech Summit.
Claudia Perlich
Why Are Women in Data Science Invisible?
There are many excellent women in my field (I know many of them personally), but for the most part they are not on the radar for keynote speeches and rather few of them have titles like Chief Scientist. So, why do they seem rather invisible?
I got lucky. I was turned down for a job at a good business school because I was judged to be “too technical” by the (male) head of the department. In hindsight I should be eternally grateful.
One answer is that many women in data science are simply not in the right places to be seen. Most of my female data science friends have chosen to stay in academia—some because they enjoy teaching, some because it seemed like the thing they were expected to do, and others because it promised a predictable future (twelve hours of work a day for six years, and then hopefully tenure without having to move around and look for good school districts). Ten years ago, having an advanced degree in the equivalent of data science was not exactly sought after in industry, and few of us ventured in that direction.
The problem with data science in academia is that’s not where the magic happens. It happens at the Googles, Facebooks, Microsofts and IBMs, as well as startups like Dstillery. This is where the richest data and most interesting problems are—and it’s not accessible to most academics. In fact, for them, getting access requires heavy networking.
In this regard, I got lucky. I was turned down for a job at a good business school because I was judged to be “too technical” by the (male) head of the department. In hindsight I should be eternally grateful. I also had a “2-body problem” (the academics’ term for a husband), which severely limited my choices.
All of this pushed me into industry, which turned out to be the best place for me.
How I Found a Home and More Women Can, Too
In IBM Research (the only non-academic job to which I applied), I found an environment that was immediately appealing: collegial, open, not at all political. The problem with tenure is that it amounts to a nearly zero sum game. There are only so many positions, and all the junior faculty members are fighting over them. At IBM, however, I interviewed while 8.5 months pregnant, and from the beginning sensed an incredible support of the life choices and the needs for flexibility that come with wanting both a career and a family. The department was run by one of the most impressive women I’ve ever known, and she sent me a personal note to congratulate me on the birth of my son before I had even accepted the job. I felt at home. I took the job, and never looked back at academia.
Eventually, I found my way to Dstillery. At the time I received my PhD, the last thing I would have expected was to become Chief Scientist of a startup in digital advertising. Even at IBM, I was trying to avoid managerial responsibilities, and much preferred a stable income and time with my family to the proverbial fast-paced life with limited pay and great upside. But during the “interview” (disguised as a friendly lunch set up by my most trusted mentor) with the founders of Dstillery—formerly Media6Degress—I had an experience very similar to my early impressions of IBM. I felt at home in the presence of extremely smart and dedicated professionals. The offer I was given acknowledged my life, giving me financial security and full-time flexibility. As much as I liked my previous job, it seemed like an opportunity I would always regret not taking. I do not believe in regrets! I make decisions by my gut, and so far it has led me on an incredible journey!
At IBM, I interviewed while 8.5 months pregnant, and from the beginning sensed an incredible support of the life choices and the needs for flexibility that come with wanting both a career and a family.
One clear lesson from my personal story is that having women already in the environment, preferably in leading positions, can lead to a healthy culture that will encourage other women to join. But my experience also tells me that men can provide just as effective an environment in which women can thrive.
Now many large corporations have taken an active role in encouraging more women in our field. Women such as my colleague at Dstillery, Lauren Moores, and danah boyd, principal researcher at Microsoft and founder of the Data and Society Research Institute, and many others are very active in encouraging and mentoring young females to both “speak” and think data. Microsoft now sponsors an annual KDD Women of Machine Learning Breakfast. The breakfast this year was led by some of the most well-respected machine learning and data science researchers and engineers from Microsoft and provided a great networking opportunity for women in our field. Likewise, Bosch actively invited minorities to KDD by providing travel and registration support for the Broadening Participation in Data Mining (BDPM) workshop, the goal of which is “to foster mentorship, guidance, and connections of minority and underrepresented groups in data mining, while also enriching technical aptitude and exposure.”
These are very welcome and encouraging signs. The ultimate scenario, of course, would be gender parity or, more preferable to me, true gender blindness. Our goal is a world where women leaders come easily to the mind of data scientists of both genders, and I no longer have to wonder whether I have been invited primarily because I am a woman. Obviously, we have a long way to go. But if those of us who have blazed the trail for women in data science continue to recruit and guide new members to our ranks, we can get there.
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