Diversity in Data Science in 2022?

Datascience is still a terrible culture to be female.

Even as Datascience attracts a generation and is adding to the talent pool in machine learning, Datascience has an image problem. Companies are increasingly looking to hire the best and the brightest in this field, but to many talented young people, it’s not an attractive career option.

How could this be? The grim reality in tech and STEM fields continues. The data science industry is lined with biases and practices that devalue the presence and successes of women and people of color. Even your political views, approach to gender or disabilities (including mental health), could make life difficult for you.

We can rally to the defense of Ukraine, an independent nation under siege, but we somehow can’t rally behind women and people of color who just want equal access, equal pay and equal opportunities to lead (and to be promoted) in fields such as Datascience.

The lack of representation of minority groups in technical and leadership positions is an issue rooted in many industries.

There is a widespread perception among STEM students that data science, the rapidly growing field behind the artificial intelligence (AI) and big data revolution, lacks purpose and promotes an unappealingly “nerdy” work culture. Women for whatever apparent reason, for the most part don’t feel safe or attracted at scale to pursue a careers in programming, datascience, machine learning and robotics. The problem is this will only enhance bias in our systems.

The Bro Culture in Tech is Real

From quantum computing to machine learning startups, you can easily tell at a glance the majority of engineering and leadership positions are mostly men. These perceptions—some of which, unfortunately, reflect the state of datascience in many companies—have a disproportionate impact on the career choices of female students.

I’m more likely to make it as a male Teacher or Nurse, than a woman is to thrive in datascience. This is a great pity in 2022.

Black, Latinx and women are severely underrepresented in programming and datascience students. We know South Asian and East Asian men tend to do a lot better. Think about it, we can say that the population of Black and Latino sums up to around 32% of the total population in America. However, around 12% of them were enrolled in the Data Science program. That’s a huge lack of diversity and inclusion in the field of datascience.

Data Science students come from a highly educated background. As women all over the world are the most educated generation of women in the history of civilization, why are they not choosing paths in programming, datascience and machine learning?

Research from NCSES (National Center for Science and Engineering Statistics) shows that these minorities, including Native Americans, really have a low share in the S&E (Science and Engineering) occupations. Back a few years ago it was trendy to talk about diversity in Technology and especially women in technology, but not all that much has changed.

The Leadership and Culture is Toxic to Women and Minorities

From middle school through graduate school, girls and women are said to “leak” out of computer science, math and other fields that typically lead to careers in data science.

Women who have succeeded in these fields think otherwise. The pipeline isn’t leaky so much as it’s toxic, they say, lined with practices that can devalue the presence and successes of women and people of color. Is this your experience in the field today or as a student?

The churn rate is higher for women, and people of color in datascience, engineering, programming and machine learning. So, if you happen to be a woman and on top of that Black, Hispanic/Latino, or Native American, good luck to you. You have to fight an uphill battle just for equal pay and to be treated the same as your male and white colleagues. Of course it’s not cool to actually admit this.

Datascience is Not a Sexy Job Without Being Inclusive to Women

The Harvard Business Review a decade ago called datascience “the sexiest job of the 21st century.” Well it ain’t that sexy if women aren’t doing it! Having a bro-culture doesn’t exactly inspire inclusion and diversity or create an environment where bias in data-sets and language models are being tackled seriously.

So the cycle continues, the fundamental lack of diversity in data science perpetuates AI bias and this could scale into a much larger problem.

How Many Data scientists are Female?

For example, while women comprise one-quarter of the employees in the tech industry, which in itself is already quite low, one recent study found that only 15% of data scientists are women.

In a 2020 report, global management consulting firm BCG reported that women make up just 15-22 per cent of the work force in data science. Increasingly, companies are establishing leadership positions such as chief data officer, but most of those positions are held by men (2021). In fact women typically can only enter the C-suite in the same predominantly soft roles.

According to Zippia, among Data Scientists, 19.7% of them are women compared to 65.2% which are men.

The Pay Gap in Datascience is Shocking

With such a high pay-gap isn’t not exactly very much incentive for women to face all that bias and discrimination working their way up the corporate ladder now isn’t it?

The more senior you get in Engineering roles, the less women there are. So even this 20% aren’t represented in positions of leadership in a way that you’d hope or expect.

Just 5% of Data scientists in America are Latinx.

The consequence is a lack of diversity: as few as 15% of data scientists today are women. That exact number varies with the year, cascading between 10% and 20%. And that lack of diversity is a serious issue.

AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences. The teams should always mirror the customers and users, otherwise you are prone to skewing your business and products in ways that could perpetuate bias.

Sexual Harassment and Unequal Opportunity for Women Persits

In the #MeToo movement we learned how much sexual harassment women face and we know this is elevated in jobs in technology. Women are sexualized even as coders. . Fifty percent of women in STEM report they have experienced discrimination on the job while 41 percent of women in other fields have experienced workplace prejudice. How many women in tech have faced bias in promotions and have churned from the industry in disgust? It’s hard to even have good data on that, because it’s only recently we even started caring.

So what does data tell us about women in Datascience anyways? Moreover, the studies indicate that “As data science professionals advance in their careers, the percentage of women decreases significantly. Among the most advanced individual contributors, 6% of data scientists are female; 10% of executive managers are female.”

A Ubiquitous Trend - but just not Open for Women

Data science—the harnessing of big data, advanced analytics, and AI—will soon be everywhere. If women aren’t represented in the people who handle the data, what do you suppose will happen? The Data Science Diversity Gap is not really being fixed. This could have profound ways in which A.I. is biased and perpetuates a less inclusive world.

Data scientist, data engineer, machine learning specialist, analytics software engineer: these data science–related roles are in high demand. But to tell a young female student of the reality they will face in the field is a nearly painful process. Men in the field needs to understand what their female co-workers are actually up against.

We don’t need more success stories of women in STEM, engineering, programming or datascience we need a less toxic environment of men, corporate culture and access to positions of leadership where we can make the culture less toxic. You can only change culture from the inside of leadership has equal representation and a strong commitment to promote inclusion and diversity, and not just as PR.

How do you see the future of inclusion and diversity in datascience, programming and machine learning?

Data scientists typically work in highly competitive and male dominated industries and corporate cultures. Think about where datascientsts tend to work:

Without changing the culture in those industries and types of companies, we are closing the door to more female and non-whites in engineering roles such as programming, datascience and machine learning.

Venture Capital and Silicon Valley Remain Male Dominated Perpetuating Old Standards in Leadership

It starts at the top, at Venture Capital firms and in Silicon Valley and New York City. If those refuse to budge to women and inclusion, corporate American simply perpetuates the same work-culture biases, pay inequality and toxic patterns of behavior. It’s a jungle out there, especially if you are a woman in tech.

In addition, while specific figures are not available for data scientists with disabilities or LGBTQ data scientists, we do know that “There are 20% fewer LGBTQ individuals in government STEM-related jobs than should be expected.” If you cannot fix the toxic culture for women, how do you get more inclusion for Black, latinx, disabled or other kinds of minorities in America? You have to wait for a lot of while male engineers to retire, and the demographic diversity of GenZ to kick in organically.

It’s been a long wait for women in STEM, and it’s definately going to be the same for women entering the field of datascience today. You can tune it out, but the bottom line of discrimination and facing more obstacles (including sexual harassment) is a very real daily reality for women in STEM fields and datascience.

Datascience can be a fairly stable job with longer tenures than many other positions in tech. Next time you collaborate with a female programmer, data scientist or colleague, think about how their experience of corporate culture might be different from your own. If you work at a company that is toxic to minorities, it might be our civic duty to point it out.

If the entire Western world can rally for Ukraine, we should also be able to rally for people like women and minorities in datascience.