5 min read

AI is rewriting work, power, and opportunity in real time. But right now, women in AI are still underrepresented in the roles that decide what gets built, how it’s deployed, and who it helps. UNESCO has put women’s share of AI professionals at around 22% globally. That gap matters because AI isn’t “neutral.” When teams, training data, and decision-making rooms skew narrow, the outputs tend to follow. UNESCO has also warned about gender bias and harms in AI systems, from stereotyping to technology-facilitated abuse.

So let’s make this practical. If you’re exploring AI careers for women (or looking to support the women already doing the work), you need three things: the reality-check statistics, a clear map of roles, and real examples of leading women in AI you can learn from.

What are the best AI careers for women right now?

If you’re picturing an “AI career” as only machine learning engineer, zoom out. The fastest-growing opportunities for women in AI often sit at the intersection of domain expertise + data + AI systems thinking. AI is no longer confined to research labs – it’s embedded across finance, healthcare, climate, media, public policy, and almost every major industry.

“Women in AI” isn’t one job title. It includes builders, strategists, auditors, policymakers, and executives. That breadth is exactly where the opportunity lies.

Technical build roles: Data science and machine learning

Many women in data and AI begin in technical pathways. These include data science, machine learning engineering, applied research, and model evaluation. These roles focus on designing, training, testing, and improving AI systems.

Underneath most AI applications sits data infrastructure: analytics, data engineering, experimentation, model monitoring, and decision intelligence. That’s why the terms “women in data and AI” and “women leaders in data and AI” often overlap – data fluency is foundational.

AI-adjacent leadership: Product, strategy, and operations

Not every impactful AI career is purely technical. Product managers, AI strategists, and operations leaders decide what gets built, why it matters, and how it scales responsibly.

These professionals translate business problems into measurable AI outcomes. They manage deployment across teams and ensure systems deliver real-world value. For women entering AI from consulting, finance, policy, marketing, or operations backgrounds, this lane can be a powerful bridge.

Women in AI governance: Where ethics meets execution

AI governance sits between “we can build it” and “we should deploy it.” It covers risk management, privacy, compliance, safety testing, auditing, red-teaming, documentation, and alignment with emerging regulation.

A practical signal that this lane is real: UNESCO launched Women4Ethical AI, an expert platform bringing together women leaders across policy, civil society, academia, and industry.

If you’re drawn to systems thinking, accountability, and high-stakes decision-making, governance is one of the most leverage-filled places to build an AI career.

Salaries & equity for women in AI

One of the most practical questions when considering AI careers for women is straightforward: how much do these jobs pay – and is the pay equal? AI roles tend to command high salaries compared to the broader tech market. But gender pay gaps still persist.

AI roles pay well – but there’s a gap

For women entering AI today, awareness is leverage. Understanding compensation dynamics early can translate into stronger positioning, smarter career moves, and long-term financial impact. Across markets, jobs in artificial intelligence and data regularly exceed average tech compensation. Data scientists and machine learning engineers – two common entry points – often earn six-figure salaries in markets like the U.S. and Western Europe. Mid-level roles frequently range from $90,000–$130,000, while senior or specialized positions can exceed $150,000–$160,000+. At leading AI labs, base salaries can stretch toward $200,000–$400,000+, with equity and bonuses adding substantial upside.

That premium reflects strong demand and limited supply of advanced AI talent. However, women in AI and data roles often earn less than men in comparable positions. Surveys suggest wage gaps in the 10–20% range, and broader tech data shows women earning roughly 80–85¢ on the dollar compared with men. Fewer women in senior roles, bias in hiring and negotiation, and unequal access to high-visibility projects all contribute to the disparity.

Why this matters

AI may be lucrative, but pay gaps compound over time – affecting promotions, leadership access, and long-term earnings. For women building careers in AI, understanding compensation trends is strategic.

It means choosing high-value specializations, preparing data-backed negotiations, and targeting organizations that prioritize transparency and equity.

Progress is happening. Structured pay frameworks and formal equity reviews are narrowing gaps in some firms. But compensation in AI goes beyond earning power. It’s about ensuring fairness as the industry grows.

How many women work in AI today?

Multiple sources point to the same core story: women are still a minority in the AI workforce. UNESCO states that only about 22% of AI professionals are women. Independent labor-market analysis aligns with that statistic. The gap also shows up in who uses AI tools at scale. Research found women made up 42% of average monthly users of ChatGPT’s website (Nov 2022–May 2024), and 27% of app downloads in the same period.

Because AI systems don’t just automate tasks. They automate judgments: who gets seen, who gets trusted, who gets flagged, who gets hired, and who gets denied. If the people building those systems aren’t representative, the blind spots get baked in. UNESCO has been blunt about gender bias and harms in AI.

In its work on AI ethics, it highlights risks ranging from discriminatory outcomes to technology-facilitated gender-based violence.

There’s also a job-market angle. A UN International Labour Organization report covered by Reuters found AI is more likely to transform roles in female-dominated occupations than male-dominated ones in high-income countries (9.6% vs 3.5% in the report’s estimates).

Who are some leading women in data and AI?

First, let’s take a moment to see which aspirational figures are leading the charge of women in AI. By studying these top examples, you’ll have direction as to who’s changing the field or narratives around the area.

1. Fei-Fei Li

Fei-Fei Li has been central to modern computer vision and now focuses heavily on human-centered AI. She is a Stanford professor and a founding co-director of Stanford’s Human-Centered AI Institute.

2. Joy Buolamwini

You also have Joy Buolamwini has pushed the industry to confront bias in AI systems. Her work through the Algorithmic Justice League helped make “bias auditing” part of the mainstream AI conversation.

3. Timnit Gebru

Timnit Gebru has shaped how the field thinks about algorithmic bias and accountability, and she founded DAIR, an independent research institute centered on the real-world impacts of AI.

4. Rafif Srour

Rafif Srour, Dean of Programs at IE School of Science & Technology, is recognized as a leading female figure in AI as she directs and shapes data-driven and AI-related programs at the school.

How does IE School of Science & Technology support women in data and AI?

Want to boost your career as a woman in AI? You should consider studying at IE School of Science & Technology. Our institution has built women-focused infrastructure that connects directly to STEM and AI pathways. For example, you can access IE Women’s Unit initiatives through its work around building confidence, community and external impact, with an action plan designed to expand women’s opportunities and representation. Or you can zoom in on IE initiatives for women in STEM.

On the science and technology side, IE School of Science & Technology has run initiatives designed to encourage and support women in STEM, including mentorship and events that explicitly engage women in technical fields.

If you’re looking specifically at programs tied to data and AI, IE School of Science & Technology also highlights women-focused financial support in STEM-adjacent pathways, including scholarships referenced within admissions information for data science-related programs. With programs including the Master in Business Analytics & Data Science, Master in Computer Science & Digital Innovation, Master in Computer Science & Business Technology and the Master in Financial Technology, you’ll gain industry-specific knowledge to help you launch your career.

Interested in tech? Read our guide on top women in data science networks.

Want to see how we’ll support your professional growth? Read about our tech mentorship program.