6 Influential Women Who Shaped Data Science (2026)

Data science is often described as a modern field, but the core work is older than the job title. Collecting evidence, testing ideas, and communicating results have shaped decisions for generations. In 2026, those foundations matter more than ever because data and AI affect everyday products.

This post highlights six women whose work influenced the tools, methods, and mindsets behind modern data work. It is not a complete history, but it is a practical starting point if you want role models and real lessons.

Women in Tech Series: Data Science

Women have been underrepresented in tech for decades, and data roles reflect that pattern too. The gap is driven by a mix of barriers: access to early STEM opportunities, fewer visible role models, and workplace structures that can slow progression.

Progress is strongest when education, hiring, mentoring, and culture improve together. That includes equitable pay practices, flexible learning and work options, and teams that treat inclusion as day-to-day work, not branding.

What "data science" means in 2026

In most organizations, data science sits at the intersection of statistics, software, and domain knowledge. Titles vary across companies, but the shared skill is turning messy information into decisions you can explain.

If you are building skills now, focus on fundamentals that last: clear problem framing, solid data handling, and honest evaluation. Tools will change. Your ability to reason with data is the durable part.

Communities that support women in data

Mentorship and community can make a real difference, especially early on. They can help with feedback, confidence, and seeing what good career paths look like in practice.

A few established initiatives frequently recommended in the data community include:

• Women in Data Science (WiDS) for global events and resources
• Women in Machine Learning (WiML) for community and mentorship
• AnitaB.org for programs and networks supporting women and non-binary technologists
• AI4ALL for expanding access to AI education

Six women whose work shaped data science and AI

Data science is broader than one job title. The people below include mathematicians, statisticians, software engineers, and AI researchers. Their work helped define what data-driven work can look like.

1) Margaret Hamilton

Margaret Hamilton led teams building mission-critical software at a time when software was often treated as secondary. Her Apollo-era work helped prove that rigorous software processes matter in high-stakes systems.

She is also widely credited with helping popularize the term "software engineering". That framing influenced how organizations valued software work and invested in it.

Why her work matters for data teams:

• Reliability matters when models and dashboards influence real-world decisions
• Testing and failure handling are part of responsible engineering
• Clear definitions can shift how teams are funded and supported

2) Katherine Johnson

Katherine Johnson was a mathematician whose trajectory calculations supported key NASA missions, including early Project Mercury flights. Her career is a reminder that data-driven work has always been central to technical progress, even before modern labels.

Johnson also broke barriers in spaces where Black women were often excluded. That context is part of the achievement: excellence plus persistence in the face of structural limits.

Why her work matters for data teams:

• Verification is a skill, not a formality
• High-quality analysis depends on both math and careful assumptions
• Representation changes who gets to contribute to mission-defining work

3) Florence Nightingale

Florence Nightingale is best known for modern nursing, but she also used statistics and visualization to argue for better health outcomes. Her diagrams made complex mortality data understandable to decision-makers and supported reforms in sanitation and care.

Her approach still feels current in 2026. Strong analysis can fail to create change if the results are not communicated clearly and responsibly.

Why her work matters for data teams:

• Good record-keeping is the start of good analysis
• Visualization can persuade when it is accurate and accessible
• Data can drive policy when it connects to practical actions

4) Dr. Fei-Fei Li

Dr. Fei-Fei Li is known for helping establish ImageNet, a dataset and benchmark that accelerated progress in computer vision. Her work highlights a recurring truth in AI: breakthroughs often come from better data, not just better algorithms.

She has also been a visible advocate for human-centered AI and widening participation in AI education, including co-founding AI4ALL. That combination of research and access-focused work is increasingly important as AI becomes mainstream.

Why her work matters for data teams:

• Data quality and labeling choices shape model outcomes
• Benchmarks influence what the field optimizes for, so they should be designed carefully
• Technical leadership can include ethics and education, not only product delivery

5) Dr. Jeannette M. Wing

Dr. Jeannette M. Wing's 2006 essay on "computational thinking" helped popularize the idea that core CS problem-solving methods are broadly useful. That framing connects naturally to data science, where abstraction and careful reasoning show up daily.

She has also held senior research leadership roles in academia and industry. These roles helped shape how institutions think about computing, data, and society.

Why her work matters for data teams:

Computational thinking supports decomposition, modeling, and evaluation

Strong data work depends on structured reasoning, not only tools

Leadership can widen access by changing how skills are taught and discussed

6) Daphne Koller

Daphne Koller has contributed to machine learning research, including probabilistic modeling. She has also been influential in expanding access to technical learning through online education efforts.

Later, she brought ML methods into healthcare and drug discovery through industry work. Her career shows how data science skills translate across domains when they are grounded in strong fundamentals.

Why her work matters for data teams:

• Probabilistic thinking helps when uncertainty is real
• Teaching and knowledge-sharing strengthen technical ecosystems
• Applied ML is strongest when it is tied to domain context

Learn data science with Code Labs Academy

If these stories resonate and you want to build practical skills, start with fundamentals and then ship projects. A structured program can help you stay consistent, especially if you are switching careers.

Explore our Data Science & AI Bootcamp and build momentum through our Free Tech Workshops.

A simple next-step plan that works in 2026

Learn Python and SQL well enough to clean and query real datasets

Practice useful statistics: distributions, sampling, and model evaluation

Build 2 to 3 portfolio projects that answer clear questions and explain trade-offs

Join a community for feedback, accountability, and mentoring

Note: This article celebrates influential figures, but it does not claim they all held the modern job title "data scientist". Data science draws on many disciplines, and their work is part of that foundation.