Women in Data Science: Statistics, Scholarships & Resources

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A data scientist working at her desk with a desktop and laptop computer.Data science — the harnessing and interpretation of big data for decision-making purposes — can seem like an abstract or theoretical concept, but it impacts our daily lives in myriad ways. Many of the modern-day conveniences we take for granted, from a simple Google search to online shopping, are influenced by data science. And over the past decade, organizations across the public and private sectors have begun to realize its transformative power.

However, the field is plagued by a serious lack of gender diversity: Women represent only a small fraction of data scientists. More than just an issue of equality, the dearth of women in data science compromises the very value of data and the processes it powers, like artificial intelligence (AI) and machine learning.

Conversely, encouraging more women to enter the data science field can narrow its troubling gender gap and at the same time produce more holistic results. While the present state of women in data science is less than ideal, a number of available resources, including scholarships and networking opportunities, are aimed at making the field more inclusive.

Women in Data Science Statistics

Digging into the numbers, the gender disparity in data science is striking. A recent global diversity report from data analytics firm Harnham found that women represent only about 20% of all data scientists, an increase of 4% from the previous year, but still the lowest among the STEM-related fields included in the report.

The numbers are equally (if not more) discouraging when it comes to women in senior-level data science roles. According to Harnham, women are far more likely to be in entry-level positions versus leadership roles, and 13% of respondents reported having no women on their leadership teams.

The gender disparity is also reflected in compensation levels, where data science has one of the highest gender pay gaps, Harnham reports. According to the report, women in data science roles earn nearly 20% less than their male counterparts, perhaps owing to the disproportionate number of entry-level positions women occupy.

Some additional women in data science statistics further underscore the scope of the field’s gender divide, as well as that of other science, technology, engineering, and math fields:

  • Approximately 11% of data teams don’t have any women on them, according to Harnham.
  • Employment in technical roles at some of the biggest technology companies in the world — Apple, Google, Facebook — disproportionately favors men, with women working in just over 20% of those roles, Reuters reports.
  • Women make up 55% of university graduates across the globe, but account for only a third of STEM degrees, according to data from the National Center for Education Statistics.

Looking beyond data science specifically, women fare a bit better across all STEM-based careers, where they account for approximately 27% of workers, according to the U.S. Census Bureau. That’s a significant leap from the 8% of women employed in STEM in 1970, though still far too low. Additionally, the gender pay gap persists across all STEM roles. According to the Census Bureau, while women working in STEM earn more than the average female worker, they still make significantly less than their male peers.

A recent report from the Boston Consulting Group exploring the issue of women in data science found that the field also faces a perception problem. According to the report, female STEM majors were far more interested in careers perceived to have tangible, real-world impact than male students, by a margin of 73% to 50%. However, nearly half of those women felt that data science was too theoretical and abstract, and therefore less attractive as a career path. For an industry seeking greater parity, that’s a tough hill to climb.

Understanding Data Bias and the Gender Gap

Data science seeks to interpret vast amounts of complex data to predict patterns and behaviors and influence decision-making. This information is used for a variety of purposes, from the design of products and services to business recruitment tools. Data scientists employ techniques like AI and machine learning, which allow them to analyze and interpret far more data than a human could ever process.

However, these tools are only as good as the data that’s fed into them. Though often seen as impartial, and therefore more “fair,” AI can be just as biased as the human intelligence behind it. AI algorithms are created using historical data, such as a company’s past hiring practices. If the data reflects social inequities, the AI may learn and perpetuate those biases. So while AI can help identify and mitigate the impact of human biases, it can also make them worse by programming them into algorithms and deploying them at scale.

A data science workforce that skews overwhelmingly male runs the risk of perpetuating existing biases and baking them into AI algorithms that fuel things like hiring practices. This is obviously bad for women seeking employment in the field, but it’s in fact a problem for everyone, because it can produce inaccurate results and reduce people’s ability to participate in the economy.

One of the more notable examples of data bias involved Amazon’s decision to scrap a hiring algorithm after discovering that it favored male applicants. The company’s computer models were programmed to vet applicants by observing patterns in resumes submitted over 10 years. However, because most of the resumes came from men, the program taught itself to prefer male candidates. The system favored applicants who used verbs like “executed” or “captured” — words more commonly used by men — while devaluing resumes that contained the word “women’s” and downgrading graduates of two women’s colleges in the process.

The same sort of data blind spot can negatively impact other traditionally marginalized groups, such as minorities or those with disabilities. Data bias crops up in everything from recruiting to policing. It’s even been implicated in vehicle safety — because crash test dummies are predominantly modeled after men — and voice-recognition technology. These biases are harmful not only to the groups being discriminated against but also to the wider population, because they produce distorted results and undermine trust in data science’s potential to transform business and society. Put plainly: Homogeneity is bad for business.

Data bias, specifically as it pertains to the gender gap, can be avoided by increasing the presence of women in data science. Gender-diverse teams are more likely to recognize biases and ensure that algorithms and systems that are susceptible to human bias are designed to avoid them, producing more accurate, balanced results. By including more women, employers can tap into AI’s and data science’s full potential, using it to mitigate bias in the recruitment process, improve retention of female employees, and alleviate the gender gap in tech leadership.

Fortunately, many employers have begun to recognize and address the gender imbalance by reexamining their hiring policies, eliminating selection bias in the screening/interviewing of candidates, and adjusting pay levels for new hires.

Scholarships for Women in Data Science

A number of fellowship programs and scholarships for women in data science and STEM are available, many of them geared specifically toward women and other underrepresented groups. These scholarships, often backed by large technology and venture capital companies, present excellent opportunities for women to enter the field. They include the following:

ACM SIGHPC Computational & Data Science Fellowship

This program, geared specifically toward women and minorities, aims to increase the diversity of students pursuing graduate degrees in data science and computational science. The fellowship, open to graduate students at any institution in the world, provides a $15,000 annual stipend for up to two years. It was initially backed by Intel, which provided $1.5 million in funding to get the program off the ground. Twelve students were selected as fellowship winners in 2020.

Applications for the fellowship must include the following:

  • A nomination submitted by the student’s adviser, explaining why they qualify for the fellowship
  • A CV and candidate statement submitted by the student, along with contact information for an endorser
  • A brief endorsement submitted by a current or former instructor, project supervisor, or employer who can speak to the student’s accomplishments and suitability for the fellowship

QuantHub, Women in Data Science Scholarship

This scholarship provides $1,000 to women pursuing undergraduate or graduate degrees in data science, business analytics, statistics or computer science, or computer information systems. The scholarship is backed by QuantHub, a company that provides employers with data skills assessment and training tools. Applicants must provide a letter of recommendation and answer a series of questions designed to demonstrate their achievements and leadership in the data science field.

Insight Data Science Fellows Program

Though not specifically geared toward women in data science, this program — established by education startup Insight and funded by a number of tech and venture capital companies — is open to graduates of data science programs. It offers seven weeks of postdoctoral data science training, helping graduates bridge the gap between their degree and a career in data science. Needs-based travel and living expense scholarships are also available through the program.

Google, Women Techmakers Scholars Program

This Google-backed scholarship provides $10,000 to eligible female undergraduate or graduate students pursuing a degree in computer science or a related field like data science. Scholarship recipients are also allowed to attend the Google Scholars Retreat at the company’s headquarters in Mountain View, California.

The Women in Data Science Conference

The annual Women in Data Science Conference (WiDS) presents an amazing educational and networking opportunity for women pursuing a career in data science. Begun as a small one-day technical conference at Stanford University in 2015, WiDS has since expanded into a global event spanning more than 60 countries, with 100,000 attendees participating annually online and in person.

Now in its sixth year, WiDS features more than 150 regional events around the globe, providing women with an opportunity to hear about the latest data science-related research and applications. These events range from technical talks and panel discussions to recruiting and networking opportunities with academic leaders, industry professionals, and students. The conference encourages women to establish their own national and regional networks to broaden the diversity of perspectives that are needed in the data science field.

The conference also includes several other initiatives designed to educate and engage aspiring data scientists:

  • A datathon — a data-focused hackathon — that allows participants to hone their skills using a social impact challenge
  • A podcast series featuring conversations with data science leaders discussing their work, their journeys in the field, and the lessons they’ve learned along the way
  • An education outreach program designed to engage secondary school students and stoke their interest in a career in data science, AI, and related fields

Past attendees of WiDS have said the conference helps them stay informed about the latest happenings in the data science world and promotes the inclusion of more women in data science careers. It also serves as an invaluable networking opportunity, connecting women with other data science communities all over the world.

The conference also helps clear away the stereotype that data science is exclusively the province of men. For one thing, all the WiDS workshop teachers are women. According to conference co-founder Margot Gerritsen, putting successful female data science leaders front and center helps normalize the concept for both men and women and can help further close the field’s gender gap by encouraging more women to pursue a career in data science.

The Impact of the Rise of Women in Data

According to the U.S. Chamber of Commerce, the 30 fastest-growing occupations are in STEM-related fields. Yet women are grossly underrepresented, occupying less than 25% of all STEM jobs, and even less in data science. This disparity extends to the university level as well, where more than 6.7 million men in the United States hold degrees in STEM subjects, Newsweek reports, compared to only 2.5 million women. And the numbers are even starker when it comes to graduate degrees.

However, providing women with someone to look up to has a profound impact on their perception of careers in STEM fields as viable options. According to a survey by Microsoft, having female STEM role models boosts young women’s interest in STEM careers from 32% to 52%. That’s why showcasing the achievements of women in these fields is imperative, as the Women in Data Science Conference has done. Stoking women’s interest and increasing the number of women pursuing a degree in data science can help close the gender gap among professional data scientists.

The following are some notable examples of women in data science, all of whom spoke at the 2021 WiDS conference:

  • Daniela Braga is the founder and CEO of DefinedCrowd, a fast-growing company specializing in AI solutions. Dr. Braga has two decades of experience in academia and industry. She was involved in the development of Cortana, Microsoft’s virtual assistant, and formed Voicebox Technologies’ data science team, where she introduced crowdsourcing for big data solutions.
  • Hulya Emir-Farinas is the director of data science at Fitbit R&D, where her team creates and helps deploy data-driven features to serve the brand’s tens of millions of users. She previously worked at IBM and Pivotal, where she applied algorithms to help solve complex business problems.
  • Afua Bruce is DataKind’s chief program officer. The global nonprofit uses AI and data science to address humanitarian challenges. She spent several years leading science and technology strategy and program management as the executive director of the White House’s National Science and Technology Council, as well as in a variety of positions with the FBI.

In recognition of the glaring gender disparities in data science and elsewhere, multiple initiatives have been promoted in recent years to increase the presence of women in STEM fields, backed by entities like IBM, Microsoft, and the U.S. Chamber of Commerce. In 2019, the World Economic Forum urged female role models to join accelerator programs aimed at promoting women in STEM careers.

The beneficial impact of the rise of women in data science is twofold. First, as the number of women employed as data scientists grows, so will the likelihood that other women will pursue an education and career in the field. Additionally, and perhaps most importantly, the inclusion of more women in data science and STEM careers contributes to a greater diversity of perspectives and ultimately leads to better results from a business and societal perspective, producing more holistic data and more innovative — and inclusive — products and services.

Closing the Gender Gap in Data Science

The gender disparity in data science and other STEM fields is concerning, but encouraging signs indicate that the tide is beginning to turn. Between the Women in Data Science Conference and the rise of scholarships targeting women and other underrepresented groups, more efforts are being made to close the gender gap than ever before. Increasing women’s presence in data science benefits everyone by creating a more inclusive economy that produces more innovative and effective products and services. Jobs in data science are some of the most in-demand and lucrative tech jobs out there, with an average annual salary of approximately $100,560, according to estimates from the U.S. Bureau of Labor Statistics. Students interested in a data science career who want to explore the critical role women play in this field can pursue a degree in Ohio University’s Online Master of Business Analytics program.

Recommended Readings

How Data Science Can Be Used for Social Good
How to Become a Data Engineer
Business Analytics vs. Data Science: Comparing Popular Tools and Languages

Sources:

Boston Consulting Group, “What’s Keeping Women Out of Data Science?”
DefinedCrowd, About DefinedCrowd
Discover Data Science, STEM Scholarship Guide — Tips for Finding and Applying for Scholarships to Fund Your STEM Education
Forbes, “Women Are the Key to Scaling Up AI and Data Science”
Google, Scholarships+
Harnham, “Diversity in Data & Analytics”
Harvard Business Review, “What Do We Do About the Biases in AI?”
Insight, Data Science Fellows Program
National Center for Education Statistics, Digest of Education Statistics
Newsweek, “Women in STEM: Without Female Role Models, We Risk Losing Brilliant Minds in the Field” QuantHub, Women in Data Science Scholarship
Reuters, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women”
SIGHPC, Computational & Data Science Fellowships
Silicon Angle, “WiDS Women-Led Workshops Make Data Science Education Accessible to All” TechRepublic, “The Data Science Gender Pay Gap Is Shrinking — Barely”
TechRepublic, “Why Only 18% of Data Scientists Are Women”
U.S. Bureau of Labor Statistics, Data Scientists and Mathematical Science Occupations
U.S. Census Bureau, “Women Making Gains in STEM Occupations But Still Underrepresented”
U.S. Chamber of Commerce Foundation, Light a Spark: Empowering Women and Girls in STEM Women in Data Science, About Women in Data Science
World Economic Forum, “AI-Driven Companies Need to be More Diverse. Here’s Why”