As women lose ground, diversity in tech declines

After making modest progress toward gender equity at big tech companies, women have lost ground in recent layoffs. However, emerging roles in AI and machine learning (ML) give talent leaders the opportunity to reverse this trend and bring more diversity to the tech industry. By identifying women with attributes ideal for these careers to recruit and develop, they can build a more balanced and diverse workforce for the future.

This Findem 3D Data blog delves into the present state of the US labor market, with a specific focus on software engineers and developers, particularly women who are experiencing setbacks in the tech sector amidst the AI and machine learning boom.

Women bear the brunt of tech layoffs

Big tech companies pledged to add more women to the workforce, yet the needle hardly budged in the Fortune 500 by 2023. The departments where women did gain ground - HR, finance, marketing - were disproportionately affected by recent layoffs. That has reduced many of the gains for diversity in the tech industry, particularly for gender equity.

According to Layoff.fyi, over a thousand tech companies had laid off nearly a quarter million employees by November 2023. 

The layoffs at large tech companies are disproportionately impacting women. Based on Findem 3D Data, women made up about 33% of FAANG employees in November 2023, but were 44% of laid-off employees as of November 2023.

Women have been able to make strides in representation in human resources, talent, and marketing teams, but these groups are particularly vulnerable to changing economic conditions. Protocol reported, “Tech companies that have conducted layoffs this year eliminated around half of their HR and recruiting staffers.” When Elon Musk took over Twitter, one of his first decisions was to lay off 30% of its talent acquisition team.

Women are underrepresented in emerging AI and ML roles

The same companies laying off tech workers continue to struggle to fill roles associated with AI. In contrast to shrinking HR and talent departments, recent data from Indeed showed that job postings in June related to generative AI grew around 14% year over year. 

As women lose ground at FAANG companies, they also show even lower representation in booming AI and machine learning roles. In the US, only 18% of people working as an AI or ML engineer are women. As large tech companies continue to rebalance their workforce, they have shed gains in representation and expanded groups where women are few and far between.

That said, some companies have made progress in reducing the gender gap for these roles. According to the Talent Market Insights Report for Q4, of top employers for AI and ML roles, Intuit has the lowest gender gap at 42%, compared to the 80% gap at most top companies.

Strongest participation in the workforce by women ever

In 2023, there's been a notable and encouraging trend — the highest-ever participation of women in the workforce. The share of working-age women between 25 and 54 who are either actively working or seeking employment has surged to an all-time high of 77.8%.

A significant part of this resurgence can be attributed to the growth in industries traditionally dominated by women, such as nursing and teaching. However, it's essential to note that certain well-paying fields, including construction and tech management, remain male-dominated. 

The pandemic brought about unique challenges for women, especially mothers who experienced employment setbacks at three times the rate of fathers early in the pandemic. However, the recent resurgence of women in the workforce indicates a robust determination to regain lost ground and achieve greater gender parity, particularly in emerging fields like AI and machine learning.

Hiring women for AI and ML engineering roles

Looking beyond the usual set of job titles and degrees to source for hard to fill AI and ML engineering roles is an opportunity to expand representation through diversity recruiting. There are women in the workforce and possibly inside your own organization who have the right attributes for these emerging roles, but have not yet made the leap. 

Findem’s Talent Data Cloud uses 3D data to identify women with the skills, backgrounds, and experiences who haven’t been able to break into the field yet, but could make a seamless transition. About 2 in 5 women who are currently AI or ML engineers were formerly software engineers, and there are 45x more women software engineers than women AI or ML engineers in the US.

Affinity, a relationships intelligence platform for sales teams, used Findem to create a more balanced slate of candidates for engineering and other roles. They achieved their 30% goal the first year and raised the bar to 40% in 2023, then met that high bar.  

Women in high income countries are more at risk of losing their jobs due to automation than men. An ILO report estimates that 8.5% of women are in roles with high automation potential versus 3.9% of men. To ensure the continued participation of women in the workforce and capitalize on their potential, proactive and inclusive recruiting and upskilling opportunities are needed to draw more women into AI and ML roles.

Gender parity and hiring for the AI future

The state of the US labor market in Q4 highlights a concerning disparity. The tech industry continues to grapple with layoffs and the broader labor shortage. Yet, it is evident that women, who have made significant progress in tech, are now facing the risk of losing hard-won ground. The data indicates that layoffs at large tech companies are disproportionately affecting women, who already constitute a minority within these organizations. 

The underrepresentation of women in AI and machine learning roles raises additional concerns and an opportunity for change. Just 18% of individuals in these emerging positions are women. But many of these roles are going unfilled due to a lack of qualified candidates.

Bridging the diversity in tech gap with 3D data

Companies can address this issue and expand representation in these critical fields through hiring and internal mobility. It is imperative for tech companies to look beyond keyword and Boolean searches in direct sourcing channels to identify talented women who can transition into AI and ML engineering roles. 

You are not just fostering diversity and resilience in the tech workforce, but also advancing opportunities for women in the future. The challenge remains clear. As talent leaders in a time of uncertainty and change, it is time to bridge the gender gap and ensure that women are not left behind in the rapidly advancing AI and machine learning industry.