The need for gender and racial equality in the workplace is paramount for a successful organization, and data science fields are no different.
The tech industry has historically been dominated by white males, and is a space that contains an ever-lasting need for diversity in its jobs. The research on the positives of a diverse workplace is massive. From gaining wider perspectives and solutions to problems, to reducing groupthink, having a diverse workforce can be incredibly important to the success of an organization.
We at Data Science Salon had the wonderful opportunity to speak with Noelle Silver, an Executive at IBM, and have her share her perspectives as a female tech leader, her leadership style and how to get more women interested in the field.
DSS: Thank you so much for taking your time to share your experience with other women in data. Before we get started, can you tell us a little bit about yourself?
"My name is Noelle Silver, and I am an executive at IBM in the area of A.I. and Analytics, and I also founded a leadership institute around artificial intelligence focused on board and executive education, and most recently I just started a non-profit to help women and minorities get involved in the technology space."
DSS: What is your leadership style? Is it different from your male counterparts?
"My leadership style isn’t intentionally different from my male counterparts. I actually learned most of them from the male leaders that I’ve had, and I call them “Mindful Leadership Techniques”. One of them is “people over product”.
Now, granted I said I learned from my male counterparts, but it doesn’t mean that they necessarily were “good” examples right? We always get good and challenging examples that help teach us the types of things that we wanna do as leaders. I am also a big believer in calmness over conflict. I’ve had incredible leaders that are calm in the face of adversity, but I’ve also had the yellers and the screamers and the people who panic in challenges. So I’ve learned from both of them and really changed how I deal with my teams and how I even create my teams to accommodate that."
DSS: Who inspired you to be a leader, and why?
"So, I would have to say it goes back to my earliest days right? My parents, my dad specifically raised me on the golden age of science fiction, and taught me a lot about not just the science and technology part, but the metaphysical part, the sociology part, the human part, and it made me really interested not just in leadership and helping build humans, but also augmenting the human capability with artificial intelligence. It’s exactly how I got there."
DSS: How do we calculate the positives of having women in the industry?
"Some of the most positive impacts are most easily identified by creating KPIs, right? One of the things I teach all of the female leaders that I work with and mentor, is how important it is to actually drive results, and that those results are measurable. I remember reading a book “Measure What Matters” and it gave me the tools I needed to augment my “Noelle” kind of leadership style with very specific measures that drive success."
DSS: What can be done to encourage women to pursue a career in data?
"I think one of the most interesting things about data science is that it’s very much human science too. It’s understanding human behavior and interactions and really being empathetic to the humans that will use this technology.
So, I actually found some of my favorite data scientists were not born and classically trained in data. They came from sociology, or genetics or other human sciences or literary sciences where you learn to compliment the work of technology. That human part, we always call it “human in the loop” is critical.
We also have lots of different areas of technology that need people that are not just coders and not just machine learning engineers or data scientists. We need people who can manage data scientists, who can translate data science into business outcomes. It's almost as important as the models themselves. There are many different opportunities available in data science, it’s a very inclusive space. I remember at my time at Alexa, most of the women I worked with were linguists, right? I think we had 70% women on my team. So, it’s not necessarily a challenge to get women into the workforce, it’s more about educating them and letting them know that it’s a possibility."
