{"id":44344,"date":"2026-03-03T12:07:39","date_gmt":"2026-03-03T11:07:39","guid":{"rendered":"https:\/\/www.ie.edu\/uncover-ie\/?p=44344"},"modified":"2026-03-25T16:41:39","modified_gmt":"2026-03-25T15:41:39","slug":"women-in-ai-careers","status":"publish","type":"post","link":"https:\/\/www.ie.edu\/uncover-ie\/women-in-ai-careers\/","title":{"rendered":"Women in AI: Careers, challenges and salaries"},"content":{"rendered":"<\/span> 5<\/span> min read<\/span><\/span>\n

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\u2019s deployed, and who it helps. UNESCO has put women\u2019s share of AI professionals at around 22%<\/a> globally. That gap matters because AI isn\u2019t \u201cneutral.\u201d When teams, training data, and decision-making rooms skew narrow, the outputs tend to follow. UNESCO<\/a> has also warned about gender bias and harms in AI systems, from stereotyping to technology-facilitated abuse.<\/p>\n\n\n\n

So let\u2019s make this practical. If you\u2019re exploring AI careers<\/a> 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.<\/p>\n\n\n\n

What are the best AI careers for women right now?<\/h2>\n\n\n\n

If you\u2019re picturing an \u201cAI career\u201d 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\u2019s embedded across finance, healthcare, climate, media, public policy, and almost every major industry.<\/p>\n\n\n\n

\u201cWomen in AI\u201d isn\u2019t one job title. It includes builders, strategists, auditors, policymakers, and executives. That breadth is exactly where the opportunity lies.<\/p>\n\n\n\n

Technical build roles: Data science and machine learning<\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Underneath most AI applications sits data infrastructure: analytics, data engineering, experimentation, model monitoring, and decision intelligence. That\u2019s why the terms “women in data and AI” and “women leaders in data and AI” often overlap – data fluency is foundational.<\/p>\n\n\n\n

AI-adjacent leadership: Product, strategy, and operations<\/h3>\n\n\n\n

Not every impactful AI<\/a> career is purely technical. Product managers, AI strategists, and operations leaders decide what gets built, why it matters, and how it scales responsibly.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Women in AI governance: Where ethics meets execution<\/h3>\n\n\n\n

AI governance sits between \u201cwe can build it\u201d and \u201cwe should deploy it.\u201d It covers risk management, privacy, compliance, safety testing, auditing, red-teaming, documentation, and alignment with emerging regulation.<\/p>\n\n\n\n

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

If you\u2019re drawn to systems thinking, accountability, and high-stakes decision-making, governance is one of the most leverage-filled places to build an AI career.<\/p>\n\n\n\n

Salaries & equity for women in AI<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

AI roles pay well – but there\u2019s a gap<\/h3>\n\n\n\n

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\u2013$130,000<\/strong>, while senior or specialized positions can exceed $150,000\u2013$160,000+<\/strong>. At leading AI labs, base salaries can stretch toward $200,000\u2013$400,000+<\/strong>, with equity and bonuses adding substantial upside.<\/p>\n\n\n\n

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\u201320% range<\/strong>, and broader tech data shows women earning roughly 80\u201385\u00a2 on the dollar<\/strong> 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.<\/p>\n\n\n\n

Why this matters<\/h3>\n\n\n\n

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

It means choosing high-value specializations, preparing data-backed negotiations, and targeting organizations that prioritize transparency and equity.<\/p>\n\n\n\n

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\u2019s about ensuring fairness as the industry grows.<\/p>\n\n\n\n

How many women work in AI today?<\/h2>\n\n\n\n

Multiple sources point to the same core story: women are still a minority in the AI workforce. UNESCO states that only about 22%<\/a> of AI professionals are women. Independent labor-market analysis<\/a> aligns with that statistic. The gap also shows up in who uses AI tools at scale. Research<\/a> found women made up 42% of average monthly users of ChatGPT\u2019s website (Nov 2022\u2013May 2024), and 27% of app downloads in the same period.<\/p>\n\n\n\n

Because AI systems don\u2019t 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\u2019t representative, the blind spots get baked in. UNESCO has been blunt about gender bias and harms in AI.<\/p>\n\n\n\n

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In its work on AI ethics, it highlights risks ranging from discriminatory outcomes to technology-facilitated gender-based violence.<\/h3>\n<\/div>\n\n\n\n
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There\u2019s 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%<\/a> in the report\u2019s estimates).<\/p>\n\n\n\n

Who are some leading women in data and AI?<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

1. Fei-Fei Li<\/h3>\n\n\n\n

Fei-Fei Li<\/a> 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\u2019s Human-Centered AI Institute.<\/p>\n\n\n\n

2. Joy Buolamwini<\/h3>\n\n\n\n

You also have Joy Buolamwini<\/a> has pushed the industry to confront bias in AI systems. Her work through the Algorithmic Justice League helped make \u201cbias auditing\u201d part of the mainstream AI conversation.<\/p>\n\n\n\n

3. Timnit Gebru<\/h3>\n\n\n\n

Timnit Gebru<\/a> 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.<\/p>\n\n\n\n

4. Rafif Srour<\/h3>\n\n\n\n

Rafif Srour<\/a>, 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.<\/p>\n\n\n\n

How does IE School of Science & Technology support women in data and AI?<\/h2>\n\n\n\n

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\u2019s Unit<\/a> initiatives through its work around building confidence, community and external impact, with an action plan designed to expand women\u2019s opportunities and representation. Or you can zoom in on IE initiatives for\u00a0women in STEM<\/a>.<\/p>\n\n\n\n

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

If you\u2019re looking specifically at programs tied to data and AI, IE School of Science & Technology<\/a> 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<\/a>, Master in Computer Science & Digital Innovation<\/a>, Master in Computer Science & Business Technology<\/a> and the Master in Financial Technology<\/a>, you’ll gain industry-specific knowledge to help you launch your career.<\/p>\n\n\n\n

Interested in tech? Read our guide on top women in data science<\/a> networks.<\/em><\/p>\n\n\n\n

Want to see how we’ll support your professional growth? Read about our tech mentorship program<\/a>.<\/em><\/p>\n\n\n\n

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Discover our range of master’s programs at IE School of Science & Technology<\/h3>\n\n\n\n

Study in Spain with our English-language master’s degrees.<\/p>\n<\/div>\n\n\n\n

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Learn more<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Set the trend for 2026.<\/p>\n","protected":false},"author":56,"featured_media":44350,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_wpscppro_dont_share_socialmedia":false,"_wpscppro_custom_social_share_image":0,"_facebook_share_type":"","_twitter_share_type":"","_linkedin_share_type":"","_pinterest_share_type":"","_linkedin_share_type_page":"","_instagram_share_type":"","_medium_share_type":"","_threads_share_type":"","_google_business_share_type":"","_selected_social_profile":[],"_wpsp_enable_custom_social_template":false,"_wpsp_social_scheduling":{"enabled":false,"datetime":null,"platforms":[],"status":"template_only","dateOption":"today","timeOption":"now","customDays":"","customHours":"","customDate":"","customTime":"","schedulingType":"absolute"},"_wpsp_active_default_template":true},"categories":[1066,338,1092,337],"tags":[816,1019,508,1049,1032,483,489,944,474],"class_list":["post-44344","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-career-paths-planning","category-careers-outcomes","category-inclusivity","category-thought-leadership-identity","tag-master-in-business-analytics-and-data-science","tag-master-in-business-analytics-and-data-science-part-time","tag-master-in-computer-science-and-business-technology","tag-master-in-computer-science-and-digital-innovation","tag-master-in-financial-technology","tag-masters","tag-school-of-science-and-technology","tag-science-technology-and-data","tag-women"],"acf":[],"yoast_head":"\nWomen 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