iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Data science is not one job or one discipline. It includes statistics, machine learning, computer vision, product analytics, decision science, scientific computing, education, leadership and community building. The 29 women below come from those different paths.
The list originated in Analytics Vidhya’s article, published May 6, 2019 and updated for Women’s Day that year. This retrospective preserves its 29-person selection while separating historical descriptions from current information. It is a curated, non-ranked list—not a definitive measure of influence or a complete representation of women in data science.
Read the original Analytics Vidhya list for the source descriptions and 2019 context.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →How to read this list
“Blazing a trail” means making a concrete contribution that gives other people a route to follow. That contribution may be a research method, dataset, product, organization, teaching practice, public explanation or community. The original article mixed professors, executives, researchers, educators, practitioners and Analytics Vidhya contributors, so the profiles below label each person’s main area rather than implying equivalent seniority.
#1 Best Overall
- Historical context: A role described in the 2019 article is labeled as historical.
- Current status: Current affiliations are stated only where a first-party source in the available evidence confirms them.
- Selection: Inclusion reflects a documented connection to data science, analytics, statistics, machine learning, AI, education or community work—not a ranking.
For several community contributors, the available evidence establishes their inclusion in the 2019 article but does not establish a current 2026 employer or title. In those cases, that limitation is stated plainly rather than guessed.
Researchers and builders shaping AI and machine learning
Fei-Fei Li — computer vision and human-centered AI
Contribution: The original list recognized Li for foundational computer-vision and AI work. Her research helped demonstrate why large, carefully constructed datasets matter to modern machine learning.
Current status (checked 2026): Stanford identifies her as the inaugural Sequoia Professor of Computer Science, founding co-director of the Stanford Human-Centered AI Institute, and co-founder and CEO of World Labs. Her current Stanford profile also describes continuing research in AI and computer vision. Stanford profile · Stanford HAI profile
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Learn from her path: Study how datasets, benchmarks and human-centered design shape an entire research field; then examine one computer-vision paper and ask what data and evaluation choices make its conclusions credible.
Anima Anandkumar — scientific machine learning
Contribution: Anandkumar’s work connects machine learning with scientific and engineering problems, including neural operators, tensor methods, probabilistic models and non-convex optimization.
Current status (checked 2026): Caltech lists her as a Bren Professor. The institutional profile records earlier senior research leadership at NVIDIA and Amazon Web Services. Caltech profile
Learn from her path: Pick a physical system—weather, fluid flow or another domain—and compare a data-driven model with the assumptions of the underlying science.
Jeannette Wing — computer science and data-centric thinking
Contribution: The 2019 article presented Wing as an academic leader at the intersection of computer science and data science.
Current status: The available evidence does not independently verify a 2026 title or affiliation. Treat the role in the original article as historical and consult a current institutional profile before citing it.
Learn from her path: Trace how an idea from computer-science education or research becomes a practical way to reason about data and computation.
Melanie Mitchell — AI research and explanation
Contribution: Mitchell was included for research and public communication about artificial intelligence and complex systems.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteCurrent status: A current affiliation is not verified in the available evidence; the original description should therefore be read as a 2019 snapshot.
Learn from her path: Read an accessible explanation of an AI concept, then restate it in your own words without removing its limitations.
Daphne Koller — machine learning, education and entrepreneurship
Contribution: The 2019 list included Koller for work spanning machine learning research, online education and technology entrepreneurship.
Current status: No current title is independently verified in the available evidence. Use the original profile for historical context rather than assuming its 2019 role remains current.
Rank #2
- Great extension activities for science and biology
- Correlated to standards
- Comprehensive biology vocabulary study
- Fascinating true-to-life illustrations
Learn from her path: Compare the skills needed to publish a method, teach it at scale and turn it into a usable product.
Leaders applying data to products and decisions
Cassie Kozyrkov — decision intelligence
Contribution: Kozyrkov’s work made a practical distinction between building models and improving decisions. The 2019 article highlighted her leadership in data-driven decision-making.
Current status (checked 2026): Her biography presents her as CEO of Kozyr and an AI advisor. It also records her previous role as Google’s first Chief Decision Scientist, where she trained employees and promoted evidence-based decisions. Kozyr biography
Learn from her path: For every model you build, write down the decision it is meant to improve, the person who will use it and the consequence of being wrong.
Recommended Free Tools
Monica Rogati — product and applied data science
Contribution: Rogati was included as an industry data-science leader working on the practical use of data in products and organizations.
Current status: The available evidence does not verify a current employer or title. The 2019 description should be labeled historical.
Learn from her path: Turn a business question into a measurable product hypothesis, then define success and failure before choosing a model.
Emily Glassberg Sands — economics, experimentation and analytics
Contribution: The original list recognized Sands for applying analytical and economic reasoning to organizational and product questions.
Free tools Windows power users keep installed
One-click scans. No signup required.
Current status: A 2026 affiliation is not verified in the available evidence; do not present the 2019 employer or title in the present tense.
Learn from her path: Practice separating correlation from a decision-ready estimate by writing the assumptions behind an experiment.
Elena Grewal — operational analytics
Contribution: Grewal appeared in the 2019 article as a data and analytics leader applying measurement to operational and customer problems.
Current status: Her current organization and title are not independently confirmed here.
Learn from her path: Map a service process, identify where data is generated and find one intervention whose effect can be measured.
Jana Eggers — technology leadership and innovation
Contribution: The list included Eggers for leadership connecting technology, analytics and innovation.
Current status: Current affiliation is not verified in the available evidence, so the original role is historical.
Learn from her path: Study how technical evidence is translated into a product strategy that nontechnical leaders can act on.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Caitlin Smallwood — large-scale data and analytics leadership
Contribution: Smallwood was profiled as an executive responsible for science and analytics in a large technology organization.
Current status: The 2019 title and employer are time-sensitive and not independently verified for 2026.
Learn from her path: Explore how an analytics function sets standards for experimentation, measurement and responsible deployment across teams.
Yael Garten — health, research and applied data
Contribution: Garten was included for applied data-science work linking research, technology and real-world health or business use cases.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Current status: No current affiliation is verified in the available evidence.
Learn from her path: Choose a domain where errors have human consequences and document the privacy, bias and validation requirements before modeling.
Vivian Zhang — product analytics and data practice
Contribution: The 2019 list presented Zhang as an applied data professional working across analytics and technology products.
Current status: A current title or organization is not established by the available sources.
Learn from her path: Build a small dashboard that answers one user question, and test whether its metrics actually change a decision.
Educators, communicators and data-literacy advocates
Rachel Thomas — accessible deep-learning education
Contribution: The original article identified Thomas as a co-founder of fast.ai and an educator whose courses reached a global audience.
Current status: The available evidence confirms the historical fast.ai contribution but does not verify a current affiliation for 2026. Do not silently convert the 2019 description into a present-tense job title.
Learn from her path: Use a practical project to learn a concept, then explain the result to someone who does not work in machine learning.
Kate Strachnyi — analytics communication
Contribution: Strachnyi was included for making analytics careers, skills and professional practice more accessible through communication and community work.
Current status: A current title is not independently verified in the available evidence.
Rank #4
- Science Exploration for Curious Kids
- AI, STEM, and Future Technology Topics
- Illustrated Learning Through Questions
- Space and Discovery Adventures
- Building Curiosity and Scientific Thinking
Learn from her path: Publish a short explanation of one analytical method, including when it should not be used.
Kristen Kehrer — practical data-science teaching
Contribution: The 2019 list recognized Kehrer for education and practical guidance in data science.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Current status: Current affiliation is not verified here; treat the original role as historical.
Learn from her path: Reproduce a tutorial with your own dataset and record every preprocessing choice.
Carla Gentry — statistics and public analytics
Contribution: Gentry was included as an analytics practitioner and communicator who helped make statistical thinking visible to a broad audience.
Current status: The available evidence does not confirm a 2026 employer or title.
Learn from her path: Take one popular statistical claim and check its sample, uncertainty and denominator.
Sarah Nooravi — early-career data practice
Contribution: Nooravi appeared in the original list as a practitioner connected with applied data work and the Analytics Vidhya community.
Current status: A current affiliation is not verified in the available evidence.
Learn from her path: Build a portfolio project that shows the question, data cleaning, analysis, limitations and recommendation—not only the final chart.
Parul Pandey — technical writing and learning resources
Contribution: Pandey was included among Analytics Vidhya contributors for educational and technical communication in data science.
Current status: The available evidence establishes the 2019 community role but not a current title.
Learn from her path: Explain a notebook line by line and add a reproducibility checklist.
Aishwarya Singh — community education
Contribution: Singh was listed as an Analytics Vidhya contributor helping learners engage with data-science topics.
Current status: Current organization and role are not independently verified.
Best Value
- Supports NSE standards
- Students will gain extra practice with the skills they are learning in their physical, earth, space, and life science curriculums
- Grades 5-8
- Includes 96 pages
Learn from her path: Join a study group and teach one concept; teaching exposes gaps that solitary practice can hide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Community contributors and rising practitioners
The following names were part of the original Analytics Vidhya community section. Their inclusion matters because writing, peer support, project sharing and mentorship are forms of technical leadership. The available evidence does not provide verified 2026 affiliations for these profiles, so each is intentionally described in historical terms.
Pavleen Kaur
2019 contribution: Analytics Vidhya identified Kaur as a community contributor in data science. Try this: Share a reproducible analysis and invite constructive review.
Free tools Windows power users keep installed
One-click scans. No signup required.
Shilpi Bhabhra
2019 contribution: Bhabhra was included for community and applied-data participation. Try this: Turn one project into a concise case study with a clear business or social question.
Divya Choudhary
2019 contribution: Choudhary appeared among the community practitioners helping learners engage with data science. Try this: Contribute an explanation, dataset note or correction to a peer project.
Srishti Gupta
2019 contribution: Gupta was recognized in the Analytics Vidhya contributor group. Try this: Keep a learning log that records failed approaches as well as successful ones.
Mathangi Sri
2019 contribution: Sri was included as a community data practitioner. Try this: Reproduce a published analysis and compare your result with the author’s assumptions.
PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minutePrarthana Bhat
2019 contribution: Bhat was listed among Analytics Vidhya contributors. The original page contains a spelling inconsistency; “Prarthana Bhat” is used here. Try this: Present one insight with its uncertainty and an explicit next action.
Anchal Gupta
2019 contribution: Gupta appeared in the community section as an emerging data-science contributor. Try this: Seek code review and document what changed after feedback.
Preeti Agarwal
2019 contribution: Agarwal was included for participation in the Analytics Vidhya learning community. Try this: Build a small project that combines a technical method with a domain you understand well.
Tanvi Purohit
2019 contribution: Purohit was listed among the community practitioners. Try this: Explain your evaluation metric in plain language and justify why it fits the decision.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhat these careers have in common
- Technical depth and communication reinforce each other. Research, product work and teaching all require explaining assumptions and limitations.
- Data science is broader than a job title. A person may contribute through statistics, economics, computer science, operations, visualization, education or domain expertise.
- Career paths are nonlinear. The list includes movement among academia, industry, entrepreneurship, public communication and community work.
- Community work is real leadership. Tutorials, mentoring, events and peer review expand who can participate.
- Responsible use matters alongside performance. Privacy, bias, interpretability and consequences belong in the project plan, not as an afterthought.
- There is no single “women in data science” archetype. These careers show different interests, working styles and routes into technical influence.
How to follow and learn from women in data science today
- Start with a first-party source. Use an institutional biography, research page or official personal site, and check its date.
- Choose one artifact. Read a paper, watch a talk, complete a course lesson or reproduce a public project.
- Make the learning active. Write a short critique, recreate a figure or test the method on a new dataset.
- Join a community. Women in Machine Learning provides profiles, events, a community Slack and a directory. Women in Data focuses on increasing diversity in data careers.
- Find peers and mentors. Local meetups, conference mentoring and study groups often provide more sustained support than simply following accounts online.
Frequently asked questions
Is this a ranking?
No. It preserves a 2019 curated selection and groups the women by contribution. It does not claim that one person is more important than another.
Are all 29 women still in the roles listed?
No. The original article is from 2019. Current roles are confirmed here only for Fei-Fei Li, Cassie Kozyrkov and Anima Anandkumar; the remaining historical descriptions should not be read as current job titles.
What is the difference between data science, analytics and AI research?
Analytics generally focuses on describing performance and supporting decisions; data science often combines statistics, programming and domain knowledge to build analyses or models; AI research develops methods and theories, sometimes without an immediate product. The boundaries overlap, but the goals and evidence can differ.
Do I need a computer-science degree to enter data science?
No single degree is required. People enter through statistics, mathematics, economics, engineering, computer science, social science, business analytics and domain-specific careers. Employers still expect evidence of quantitative reasoning, programming, communication and responsible data use.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Frequently Asked Questions
Where did the original list come from?
It was published by Analytics Vidhya on May 6, 2019 and updated for Women’s Day 2019.
Why are some current affiliations not listed?
The available evidence does not verify a current 2026 title for every person, so historical roles are not presented as present-day facts.
The Bottom Line
The most useful lesson from these 29 careers is variety: trailblazing can mean inventing a method, improving a decision, teaching a difficult idea, building a responsible product or helping another learner enter the field. Use the list as a starting map, verify current information through first-party sources and turn admiration into a concrete project or community connection.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
