When St. Lawrence was studying for her Master’s Degree in Analytics at Villanova University, she noted that throughout the two year program she only had one female professor and only a handful of other women in her program. “Early on I realized this was really different then any world I worked in before,” she remembers. What started as a Meetup Page in 2016 laid the foundation for what would later become Women in Data, a nonprofit organization whose mission is to increase diversity in data careers by awareness, education, and training that St. Lawrence founded and is now currently the CEO.
During the meetups, St. Lawrence noted that the lack of women in data jobs wasn’t necessarily because women weren’t interested in pursuing these types of positions but rather they didn’t necessarily know they existed. St. Lawrence sited that there was a big need for coaching and educating – which gave her the idea to start a nonprofit. Since the start of Women in Data in 2016 the one chapter located in Philadelphia has become 10 chapters today spanning the United States and even venturing into international territory in Vancouver, Canada.
During our conversation St. Lawrence mentioned that we as a society are in the 4th industrial revolution. Originally coined by Klaus Schwab in his 2016, the 4th Industrial Revolution is the fourth major industrial era since the first industrial revolution in the 18th century. This revolution is driven by robotics, data, and the digital age. “How we live and work are activiely changing. The computing power for data has increased.” She continued, “We’re able now to utilize the algorithms that we never had before.” Because of this the need for data scientists has continued to rise. According to the Quant Crunch, there will be 2.7 million jobs open for data and analytics in 2020. And that’s just in the United States. St. Lawrence believes that as the data industry grows and changes so will the job of a data scientist. She suggests data scientists’ responsibilities will shift from coding to problem solving and decision making.
When I asked how she saw women fitting into the data revolution, she explained that diversity is so important because men and women focus on different aspects of data. In her studies she’s concluded that women tend to work on algorithms that revolve around people’s needs and wants. “Women want to work on data science project that are socially driven and do good back in their communities.” Men tend to target algorithms that focus on creating things. St. Lawrence stated that both algorithms are necessary to ensure the 4th Industrial Revolution goes smoothly.
Equal representation in the data field has a long way to go, but St. Lawrence is confident that through outreach and training from Women in Data, and that gap will decline. “. You can’t do everything today, over a long period of time you’ll be able to accomplish more than you know,” she explained.
