The Future of Data Science Depends on Women
Historically, women have been remarkably underrepresented in the field of data science, but that narrative is shifting.
As data science becomes one of the most in-demand and fastest-growing career paths in the country, we’re now seeing more women in data science—and the powerful impact they can have.
A recent study on the future of data science from Burtch Works, an executive recruiting agency, reveals that the number of women in data scientist roles is now at 24—an increase of nearly 10% since 2018.
Women in Data Science: Why They’re Critical
Data is shaping the future in every way. It’s changing how we live, work, and play. As more data is generated through connected machines, devices, and platforms, there’s a growing need for three-dimensional data scientists. These professionals have a deep understanding of math and statistics, can clearly translate data to actionable insights, and possess strong critical thinking skills supported by a solid ethical framework. Notre Dame’s ability to empower students with these invaluable skills is just one reason we’re named a top 10 online graduate program in data science.
Making sure women are well-represented in these roles brings many benefits. For example, it helps mitigate biases and encourages alternative viewpoints when searching for patterns and building algorithms. It also ensures that the needs of other female customers and employees are addressed.
“Women bring an advantage in this field because we have a unique viewpoint,” explains Kaitlyn Arnold ’21, advisory data and AI technical specialist at IBM. “If you’re passionate about technology and data, then data science is a great fit. Depending on what you want to do, you can be behind the computer every day, programming and collecting, cleaning, and analyzing data—or you can be in front of people every day asking questions, finding answers, and very rarely doing that type of work. Because there are so many different variations of data science, you can find your place.”
Career opportunities abound for women in data science and women in machine learning. They can fill critical roles, such as:
- Business analyst
- Business intelligence analyst
- Data analyst
- Data mining engineer
- Data storyteller
- Machine learning engineer
- Market intelligence director
- Technology intelligence consultant
Arnold found her career fit at IBM, where she sells data science and AI solutions to chief technology officers, chief innovation officers, and data science managers in the financial services market—people who know and understand big data. Instead of working behind a computer screen, she wanted to spend her time networking, helping clients find solutions, and discovering the art of what’s possible with data.
“Sometimes I’m the only woman in the room. But females in technology, and in data science specifically, have a leg up because of the perspective they bring—especially if they’re willing to speak a little bit louder to make sure their voices are heard.”
-Kaitlyn Arnold, Advisory Data and AI Technical Specialist at IBM
Start Your Own Career in Data Science
To begin or advance your career in data science, Arnold recommends looking for data science programs that put math and statistics at the forefront. “With this kind of educational background, you’ll breeze through nearly any situation,” she explains. “It lays the groundwork for everything. Foundationally, data science graduates from the University of Notre Dame are at vastly different levels than graduates from other data science programs. I was better prepared because of how this program is structured.”
She also emphasizes the importance of looking for a program that provides real-world opportunities. Arnold works with many other data science professionals on her team, but some have never made machine learning models or worked with powerful analysis tools—things she experienced while earning a master’s in data science at Notre Dame. “While you may not need that background to succeed in a role like this, it certainly makes me feel more confident,” she says, “which makes me 100 times better at my job.”
The next step is to think about the types of data science roles you might be interested in—and then find women in data science who work in those roles so you can learn more about what they do. “Those women are out there,” says Arnold. “Read, look for people on LinkedIn, or even if you hear someone talking about data science at a restaurant—make those connections.”
Finally, she encourages women in data science to get involved with nonprofit data science organizations, such as Women in Data. The group brings female professionals together to share their real-world experiences in data science roles and working with data science organizations.
Arnold got her professional start in data science as the creative strategy and growth manager for Women in Data. She also helped the nonprofit open its New York chapter. “I’ve watched the organization grow and watched people swarm to it because it’s about helping females grow and learn about the field.”
Today, she continues to support women in data science by serving as a speaker and instructor for Women in Data, even leading a Python course last year for people displaced from their jobs because of the pandemic.
For Women, the Future of Data Science Looks Bright
“I’m sure some men experience this, too, but, in one way or another, you will get knocked down,” describes Arnold. “The only way to combat that is to continuously learn, work on yourself, and grow personally and professionally.”
As data generation and usage accelerate, all types of businesses and data science organizations will be looking for the best and brightest in the field: women in data science or women in machine learning who can shape raw data into relevant stories and insights that influence decision-making and reveal how to improve business and create new offerings.
“Data science has the potential to solve some of the world’s most complex problems,” says Arnold. “The more diversity you can get in data science, the more minds you have in the room to help solve those problems. That will make the solutions better, more scalable, and more long-lasting. Having women in the room is going to change the world—and the future of data science.”
Ready to join women in data science and experience the Notre Dame Edge? Apply today.