Big data may still be a buzz word, but the term seems to have wormed its way into everyone’s vernacular. It’s permeated all facets of life, from tech and advertising companies, to health care and government. Even recruitment firms are getting in the game.
But there’s still a talent shortage – there’s so much demand, but not enough folks who are capable of analyzing the information in relevant ways.
We caught up with one of Canada’s premier data scientists, Kobo’s VP of Big Data Inmar Givoni to talk about why it’s so hard to make the transition into this space and what’s keeping more women from taking up the fight.
What are three major milestones that have shaped where you are today?
When I was 15, I took an extracurricular class on the human brain and thought it was the most fascinating topic to study. We still don’t really understand how the brain works, and studying it seemed like the most important scientific question to me. At that age I decided I wanted to become a neuroscientist.
In university I studied computer science and biology because I thought they were two areas that would help me follow the brain. But in third year, I took a course in machine learning, artificial intelligence, and I got fascinated with that topic. I thought one way of understanding the brain wasn’t just directly researching it, but trying to build machines that are as intelligent as humans – to try and understand intelligence by creating it.
And that prompted me to come down to Toronto and do a PhD specifically in machine learning.
After I finished my PhD, one decision I had to make was to stay in academia or go into the industry. I decided to pursue a career in industry because I feel what I do has a more meaningful impact to people and society.
For new people looking to break into the data science world, what do you think the biggest challenge is?
The big thing I think people are finding challenging, specifically those looking to move into this world of big data and data science is that it’s a lot more complicated than many realize.
On the one hand, there’s huge demand for data scientists. On the other hand the people who want to become data scientists and already have existing qualifications in related fields, like math or business, find it difficult to make the transitions because they don’t necessarily have the complete background.
I hear companies complaining about how difficult it is to recruit someone who can program, do the math and understand business. On the other hand, there are people who have one of these three skills and are trying to catch up with the others (maybe they took courses online or are self-taught) but find it difficult to get jobs because typically employers want to see the fundamental skill-sets of programming, math and business.
Because there is such a large demand, employers are giving employees who don’t have an ideal background a chance to prove themselves. And there are a lot of universities and academic institutions trying to build undergraduate programs or masters-level programs for data-scientists. So in a few years, I think we’ll see people coming out of these academic pipelines.
What are some of the most important skills that people in big data need today?
A typical data-scientist is someone who teaches themselves how to learn everything quickly.
So let’s move now to the hot-button issue of women in tech: what do you consider to be the biggest barrier for women getting into the STEM fields.
I think there are so many barriers I can’t decide which one is the most significant. It starts before you’re born and the expectations parents and family have. As a toddler and child, there are all sorts of gentle biases – most of them unintentional – around how they treat boys and girls differently, such as the type of encouragement you get as a boy to show interest in technical things like trains and cars.
In early high school years, they’ve developed stereotypes and stigma around what’s cool to do or what isn’t. In university, there’s a big gap around women enrolling in technical programs. That’s when women that did pursue that option are met with the reality of being isolated and surrounded by men. There are not a lot of role models, and you’re not necessarily a part of cliques or study groups. Women start developing what’s known as the imposter syndrome because they feel they don’t know as much as what these guys say they know or understand.
And then, of course once you finish university, it’s well studied that women don’t necessarily write the most sparkling resume because they’re taught not to brag. Most often they’ll choose not to pursue a job opportunity if they don’t believe they’re qualified (versus guys who will apply even if they only have a few of the qualifications).
Finally, there are unconscious biases – there are so many academic studies that show with the same resume if you switch the name of a candidate from a man to a woman (or an ethnic minority name) your chances of getting the job drops. If you get the job, the salary you’re offered drops.
Of course there is a growing awareness of these issues and a lot of people trying to deal with these problems.
Have you ever felt like you weren’t welcomed in the tech world?
I’ve never felt as though I wasn’t welcome – nobody ever told me “you shouldn’t be doing this.” But, I definitely felt that I was out of place because everyone around me was a man or was confident in what they know and what they do – though I did choose a field that was particularly lacking in women.
Until I came to Canada, I wasn’t even aware there were any issues. I’m originally from Israel – and the situation is not that bad everywhere. In Iran for example, it’s considered a suitable job for a woman to be an electrical engineer – much more than here.
How has that shaped your views of the industry?
It prompted a lot more participation in organizations that promote women. When I was doing my PhD, I got involved in an annual workshop for women in machine learning. When I went into the industry, I made sure companies I worked for were interested in improving the situation, were willing to put funding aside for sponsorships of events or encouraging mentorship. For example, at Kobo, I run a monthly event for women.
I’m a very pragmatic person: so to me, it’s a problem that needs solving. Personally, I try to get involved with initiatives I think will be helpful.