Why Aren't there More Women in Data Science?

Read on to learn more about the challenges women face in the field of data science, and how current female data scientists are creating opportunities to help bridge the gender gap in STEM fields.

women in data science

It’s no secret that STEM professions—shaped by years of gender and racial bias—lack diversity. Data science is no exception. A survey conducted by the Boston Consulting Group found that roughly 15-22% of data scientists are women, and that organizations continue to come up short in attracting and retaining women employees, even though the talent is there.

But progress, although slow, is being made. Companies are waking up to the fact that lacking diversity doesn’t just look bad, it adversely affects the bottom line. Without diverse and balanced data science teams that include women at all levels, algorithms can skew toward the biases of the dominant group, interpretations of data insights don’t have the benefit of different points of view, and companies building products and services for everyone end up with glaring blind spots.

In addition to companies making a greater effort to build and support a diverse workforce, many industry leaders have taken matters into their own hands, creating opportunities and support networks for women in data science and clearing some of the hurdles that stand in the way.

Why Aren't There More Women in Data Science?

One of the main challenges companies face in hiring more women data scientists is that the profession itself has an image problem, according to the Boston Consulting Group, which can deter women from training to become data scientists, to begin with. The image problem is fueled by a number of myths that researchers believe can be overcome.

  • The myth: Data science is abstract and lacks purpose
  • The reality: Data science, when done well, can influence the design of a product or service and change the course of an organization. Many organizations lean on data science teams to offer insights into product or service performance, develop metrics, understand user behavior, and forecast everything from sales to sign-ups. Data scientists also frequently employ artificial intelligence and machine learning to anticipate customer needs and boost efficiency, and they’re responsible for the recommendation engines that influence what we buy, watch, use, and listen to.
  • The myth: Data science is hyper-competitive and non-inclusive
  • The reality: The field of data science is associated with hyper-competitiveness because many companies look for experience with coding competitions and hackathons when hiring, even though the profession itself is better known for collaboration. In fact, competitiveness can often get in the way of a data scientist’s job because the very nature of their work is collaborative—data scientists work alongside machine learning engineers, software engineers, product managers, marketers, UX researchers, and other stakeholders in order to solve company problems. Data scientists also frequently work together to build data pipelines and develop models and algorithms. It’s a profession that rewards those who work together.
  • The myth: You need to be exceptional at everything in order to cut it as a data scientist
  • The reality: Job descriptions for data scientists often alienate potential candidates because they set an unreasonably high bar. It’s not unusual for job listings to require entry-level data scientists to know multiple programming languages, have years of experience under their belt, have technical skills in both data science and machine learning, and be familiar with every machine learning library under the sun. The reality is that many of these organizations would happily hire a strong data scientist who is willing to learn and grow.
  • The myth: There’s no room for growth; there are no women at the top
  • The reality: While it’s true that women do not occupy as many leadership positions in data science, there are many organization and individual efforts to support women in their careers and create wider and more varied paths to the top. One of the key ways this is being achieved is through both formal and informal mentorships, in which senior data scientists and managers sponsor and advocate for less experienced employees. In addition to advocacy, mentors also provide valuable insight and advice on navigating careers in data science, sharing information that might otherwise be hard to come by.

career opportunities for women in data science

What Opportunities Are Available for Women in Data Science?

Every facet of data science, whether it’s data gathering and cleaning, data analysis, forecasting, or machine learning, benefits from the input of diverse teams. Recognizing some of the barriers to entry that can deter women from careers in the profession, data science leaders have launched conferences and initiatives to support women from the time they’re in school through to when they’re in the workforce.

Some of the more notable conferences, organizations, and networking opportunities include:

  1. WIDS Worldwide Conference
  2. Women in Data Science
  3. Women in Machine Learning and Data Science
  4. Women in Technology
  5. Women in Data Science and Statistics
  6. PyLadies
  7. Black Girls Code
  8. Girls Who Code
  9. Within
  10. GirDevelopIt
  11. R-Ladies

Scholarships and Grants for Women in Data Science

A growing number of organizations have thrown their support behind helping the data science industry achieve proportional representation, namely through offering scholarships, fellowships, and grants. Data science scholarships are available for undergraduate and graduate students who are pursuing data science or related fields, and there are also paid internship programs that aim to give students from historically underrepresented groups hands-on industry experience.

On the online learning/bootcamp front, Springboard has partnered with Women Who Code to offer ten scholarships worth $1,000 each to women who enroll in Springboard’s Data Science Career Track, Software Engineering Career Track, or the Machine Learning Career Track.

Springboard also offers a number of women-in-tech scholarships.

How Does Springboard Support Women Entering Data Science?

Students who have completed Springboard’s Data Science Career Track have gone on to work as data scientists at companies like Boeing, Johnson & Johnson, Pandora, and Amazon.

Many graduates of the program credit Springboard’s three-pronged approach to their success:

  1. Ensuring students learn foundational data science skills
  2. Giving every student hands-on experience with real-world projects
  3. Comprehensive mentorship

“My first meeting with my mentor, Ike, set the tone for a very productive relationship,” said Sara Weinstein, a Springboard graduate who is now a data scientist at Boeing. “Several hours later, I got an email from him with a long list of resources tailored specifically to what I’d told him I wanted to learn. I was absolutely flabbergasted—it must have taken him at least an hour. Here was this total stranger who had taken the time to identify a whole bunch of books, PDFs, and papers just got me, that I would not have found on my own. It was just amazing!”

In addition to mentorship from an industry expert, Springboard’s curriculum is designed to be an accessible, guided learning experience that makes data science training less overwhelming.

“I had several false starts trying to teach myself R and Python because there were too many resources out there,” said Arti Annaswami, a Springboard graduate who founded her own data science consultancy. But once she started Springboard’s Data Science Career Track, she found that “it’s amazing how frictionless the learning process becomes when you’re only looking at the best material out there for a specific topic, curated by folks who are industry pioneers in that field. By the end of the course, I felt fully comfortable doing a start-to-end data workflow in R.”

Women Data Scientist Leading the Way

Despite the poor diversity numbers in the field of data science, women occupy some of the most influential and pioneering roles in the profession. A few industry leaders include:

  • Fei-Fei Li — Sequoia Professor in the Computer Science Department at Stanford University
  • Cassie Kozykov — Chief Decision Scientist at Google
  • Allie Miller — Head of AI Business Development at Amazon
  • Danielle Belgrave — Principal Researcher at Microsoft Research
  • Kate Crawford — Co-founder of AI Now Institute

“The biggest piece of advice I have for anyone is to ask for help,” Emily Bailey, a data scientist at Uber told Springboard during a panel on women in tech. “When you have a goal in front of you, it can be daunting to see how big it is, so the first place I tend to ask for help is: how do I break this up into smaller pieces? And then, even if the first piece is hard, you can ask for help every step of the way if you need to.”

“You don’t have to check every single box,” Kathy Yang, a data scientist on Airbnb’s strategy and insights team, added. “I think especially in data science, which has such a broad scope, no one’s going to be an expert in everything. So there’s always going to be things you don’t really understand, or that you can do better.”

Check out our other great content honoring women working in tech. Springboard is a proud partner of Women Who Code in addition to offering several scholarships for women in tech.

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