Fighting the gender gap in data science

As data science begins to infiltrate more and more industries, proving to be crucial for market competitiveness, women are slowly starting to see change. Here are some women, programs, and organizations leading the way

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The gender gap in STEM positions is no secret. According to the Center for Talent and Innovation:

  • 40 percent of women that work in science and technology in the US struggle to exert themselves as executives.
  • 23 percent of women in STEM in the US think a woman would never get a top position at their company.
  • 62 percent of women in STEM in the US believe their ideas are not endorsed by industry leadership and 75 percent feel their ideas are never implemented.

Women in these roles struggle to move to higher positions, and the same runs true for women in data science. However, as data science begins to infiltrate more and more industries, proving to be crucial for market competitiveness, women are slowly starting to see change. Thankfully, there are inspiring leaders for data-savvy women to look to already dominating the industry.

Women in data science leading the way

Corinna Cortes, Head of Google Research

Cortes is well-renowned for her contributions to machine learning. Along with Vladimir Vapnik, Cortes was awarded the 2008 Paris Kanellakis Theory and Practice Award for their work on theoretical foundations of support vector machines. She is currently head of Google Research, where she works on range of theoretical and applied large-scale machine learning problems.

Brenda Jorgensen Dietrich, Vice President of Data Science, IBM

Dietrich is both an IBM fellow and vice president. She headed much of IBM’s early analytics work, and “was instrumental in redirecting IBM’s research division to support services planning and management.”

Jana Eggers, CEO at Nara Logics

Eggers is the CEO of Nara Logics, a neuroscience-based artificial intelligence company focused on turning big data into smart action. Eggers has more than 25 years of business experience and currently speaks, writes, and consults on artificial intelligence and startups.

Daphne Koller, Professor of Computer Science, Stanford University

Koller is the cofounder of online learning platform Coursera, as well as the chief computing officer at Calico Life Sciences, all in addition to being part of the Stanford University faculty. Koller’s research areas are artificial intelligence and its applications in the biomedical sciences.

Hilary Mason, Founder of Fast Forward Labs

Mason served as the chief data scientist for Bit.ly for four years before founding machine learning intelligence research startup, Fast Forward Labs. Mason is also the Data Scientist in Residence at Accel Partners, cofounded HackNY, and is a member of NYC Resistor.

On the heels of the data science revolution, academia and organizations are catching on to the need for more women in the industry. There are efforts under way to help close the gender gap by teaching women crucial data science skills and preparing them to take on high-level roles within companies.

Academic scholarships and fellowships for women in data science

ACM SIGHPC/Intel Computational & Data Science Fellowships (National)

ACM SIGHPC and Intel have partnered to create Computational and Data Science Fellowships open to applicants who are “a woman and/or a member of a racial/ethnic group that is currently underrepresented in the computing field in the country where the student will earn the degree.” The fellowship is open to students who are currently enrolled in a graduate program in computational or data science. Each fellowship recipient receives $15,000 annually for up to five years.

IBM Award for Women in Data Science (National)

IBM offers $150,000 to support female participation in Galvanize programs. Galvanize, a learning community for technology, offers 12-week immersive data science learning programs across the country. The program allows students to work through real-world data sets, and for its part, “IBM has committed to provide support to female participants in Galvanize programs, from tuition assistance to mentorship, to internships and employment opportunities.”

Women in Data Science Scholarship (Melbourne, Australia)

La Trobe University’s School of Engineering and Mathematical Sciences (SEMS) in Melbourne, Australia has established the Women in Data Science Scholarship for female students in their first year enrolled in the Master of Data Science program. The scholarship offers A$5,000 per year for eligible candidates.

Workshops and conferences for women in data science

Global Women in Data Science (WiDS) Conference (Stanford, CA)

This event, held March 5, 2018, seeks to educate data scientists worldwide and support women in the field. All genders are welcome to participate at the conference, which will feature exclusively female speakers, and “provide an opportunity to hear about the latest data science related research and applications in a broad set of domains.”

Women in Machine Learning Workshop (Long Beach, CA)

The flagship event of Women in Machine Learning, an organization dedicated to empowering women in the field, is its annual workshop. The event, held this December, “gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other.”

Though there is a long way to go, the group Women in Machine Learning and Data Science (WiMLDS) is ready to support women in the field:

WiMLDS’s mission is to support and promote women practicing, studying, or interested in the fields of machine learning and data science. We create opportunities for women to engage in technical and professional conversations in a positive, supportive environment by hosting talks by prominent data scientists, technical workshops, networking events and hackathons.

While there has been a significant push for more women in STEM, data science specifically has a been a bit slower to catch up. However, with women taking higher data science roles and more scholarship and mentoring opportunities arising, hopefully, this will change.

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