Data science is not one job or one route. It includes statistical research, machine learning, product analytics, executive decision-making, education, entrepreneurship and community work. Analytics Vidhya’s original list, published by Pranav Dar on May 6, 2019, brought together 29 women across those areas. This updated retrospective preserves that historical selection while separating 2019 descriptions from information that can be verified today.
The list is curated, not ranked or exhaustive. Inclusion reflects a documented contribution to data science, analytics, artificial intelligence, research, education or professional community-building—not fame alone.
How to read this list
- Historical note: “The 2019 article described her as…” refers to the original Analytics Vidhya profile, not necessarily a current job.
- Current note: Current roles are included only where a first-party source in the available record confirms them.
- Field labels: A computer-vision researcher, product analyst, educator and community contributor may all work in the broad data-science ecosystem, but they do different work.
- Learning action: Each entry suggests a practical next step rather than treating inspiration as an endpoint.
The historical source and complete original list are available at Analytics Vidhya.
Researchers and builders shaping AI and machine learning
Fei-Fei Li — computer vision and human-centered AI
Contribution: Li’s work helped establish large-scale visual datasets and modern computer-vision research. Stanford currently 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.
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#1 Best Overall
Why it matters: Her career connects benchmark-building, scientific research, institutional leadership and the question of how AI should serve people.
Learn from her: Read her current Stanford profile and explore Stanford HAI’s research overview at Stanford HAI. Try reproducing a small computer-vision experiment, then document its limitations.
Anima Anandkumar — scientific machine learning
Contribution: Anandkumar develops methods including neural operators, tensor techniques, probabilistic models and non-convex optimization for scientific modeling and discovery. Caltech currently lists her as a Bren Professor and records previous senior research roles at NVIDIA and Amazon Web Services.
Why it matters: Her work shows that machine learning can model physical and engineering systems, not only consumer behavior or images.
Learn from her: Use the Caltech profile as a map to her research, then study one paper with a physics, climate or engineering example.
Jeannette Wing — computer science and data-centered research
Contribution: The 2019 article presented Wing as an academic leader working at the intersection of computer science and data science.
Why it matters: Her inclusion illustrates that foundational computing ideas and research leadership shape the practice later called data science.
Current status: A current institutional affiliation was not verified in the available sources; treat the 2019 description as historical.
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Learn from her: Find a current institutional or publication profile and trace how one foundational computer-science concept becomes a practical data method.
Melanie Mitchell — AI research and explanation
Contribution: The original list included Mitchell among researchers whose work examines artificial intelligence and machine-learning ideas.
Why it matters: Her career represents the value of explaining what algorithms can and cannot do, not merely increasing model size.
Current status: The 2019 source does not establish a current role. Verify any present affiliation before relying on it.
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Daphne Koller — probabilistic modeling and education entrepreneurship
Contribution: Koller was included for influential machine-learning research and for helping expand online technical education through Coursera.
Why it matters: Her path links rigorous probabilistic methods with the challenge of making advanced learning available at scale.
Current status: Coursera-era descriptions in the 2019 article are historical; no current affiliation is established here.
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Learn from her: Select a probability or machine-learning course, complete the exercises and publish a short explanation of one model.
Leaders applying data to products and decisions
Cassie Kozyrkov — decision intelligence
Contribution: Kozyrkov led Google’s decision-science work and helped employees use data to make better decisions. Her current biography presents her as CEO of Kozyr and an AI strategy adviser.
Rank #2
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Why it matters: A prediction has value only when it improves a real decision, with its costs, constraints and uncertainty understood.
Learn from her: Read her official biography, then take a familiar business decision and write down the action, evidence, uncertainty and success measure before choosing a model.
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Contribution: The 2019 list highlighted Rogati’s work applying data science to products and organizations.
Why it matters: Product data science requires defining useful metrics, designing experiments and working with people who build and operate the product.
Current status: The original employer and title are historical unless confirmed through a current first-party source.
Learn from her: Practice turning a product question into a metric, an experiment and a decision rule.
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Contribution: Analytics Vidhya included Sands among industry leaders using statistical analysis to guide organizational and business decisions.
Why it matters: Her example shows that a data career can focus on causal questions, strategy and communication rather than model novelty.
Current status: The 2019 role should be read as historical; a current affiliation was not verified here.
Learn from her: Analyze a public dataset and write a one-page memo that states the decision before presenting the charts.
Elena Grewal — operational and product analytics
Contribution: The original article described Grewal as a senior data leader working on analytics in a technology business.
Why it matters: Operational analytics connects measurement with staffing, service quality and customer outcomes.
Current status: The employer and title printed in 2019 are historical unless independently verified.
Learn from her: Build a dashboard with an explicit owner, refresh schedule and action for each metric.
Jana Eggers — analytics leadership and innovation
Contribution: Eggers was included for executive work connecting data, technology and innovation.
Why it matters: Leaders decide which questions deserve investment and how technical teams’ work reaches customers.
Current status: No current role is established by the available sources.
Learn from her: Interview a stakeholder about a business problem before selecting a dataset or algorithm.
Caitlin Smallwood — large-scale data and forecasting
Contribution: The 2019 profile associated Smallwood with senior analytics and science work in the technology industry.
Why it matters: Large organizations need reliable experimentation, forecasting and measurement systems, not isolated analyses.
Current status: The original company title is historical unless confirmed by a current first-party page.
Learn from her: Reproduce a time-series forecast and report error by segment rather than only one average score.
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Contribution: Garten’s 2019 profile emphasized applied data-science work in industry.
Why it matters: Applied research succeeds when technical methods survive messy data, changing requirements and real users.
Current status: The role named in 2019 has not been independently reverified here.
Learn from her: Keep a project log showing every data-cleaning decision and its effect on the result.
Vivian Zhang — product and business data science
Contribution: The original list recognized Zhang for practical analytics and data-science work.
Why it matters: Data practitioners often create value by translating ambiguous business questions into measurable tests.
Current status: The 2019 description is historical where no current first-party confirmation is available.
Learn from her: For one portfolio project, include a problem statement, baseline, evaluation metric and recommendation.
Educators, communicators and data-literacy advocates
Rachel Thomas — accessible machine-learning education
Contribution: The 2019 article identified Thomas as a co-founder of fast.ai and an educator whose courses reached a global audience.
Why it matters: fast.ai’s practical, code-first approach challenged the assumption that learners must master every theory topic before building useful systems.
Current status: The fast.ai description is historical context; verify current affiliations separately.
Learn from her: Work through a complete introductory lesson, then explain one result in plain language and identify its failure cases.
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Contribution: The original list included Strachnyi for making analytics careers and practice more accessible through communication and community activity.
Why it matters: Clear explanations help analysts gain trust and help newcomers see possible career routes.
Rank #4
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Current status: A current title was not verified in the available sources.
Learn from her: Turn a technical chart into a short narrative aimed at an executive or community audience.
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Kristen Kehrer — practical data-science teaching
Contribution: Analytics Vidhya highlighted Kehrer’s teaching and public communication about data science.
Why it matters: Teaching exposes assumptions and creates reusable knowledge for teams and learners.
Current status: The 2019 role is historical unless a current first-party profile confirms it.
Learn from her: Present a five-minute lesson on one modeling choice, including when not to use it.
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Contribution: Pandey appeared in the community portion of the original list for tutorials and practical data-science communication.
Why it matters: Well-documented notebooks lower the barrier between an idea and a reproducible result.
Current status: The original article does not establish a current employer or title.
Learn from her: Publish a notebook with a clear README, data provenance and an explanation of limitations.
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Contribution: The 2019 community list recognized Singh for sharing data-science knowledge with learners.
Why it matters: Peer teaching is a form of technical leadership, especially for people entering without established networks.
Current status: A current affiliation could not be verified from the available sources.
Learn from her: Help one beginner reproduce a project and record the questions that exposed unclear documentation.
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Carla Gentry — analytics practice and public guidance
Contribution: Gentry was included as an experienced analytics practitioner and communicator.
Why it matters: Public explanations of data work can connect statistical thinking with everyday business decisions.
Current status: The original profile is historical; no current role is confirmed here.
Learn from her: Compare two interpretations of the same statistic and explain which decision each would support.
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Sarah Nooravi — early-career analytics representation
Contribution: The original article included Nooravi among practitioners associated with the Analytics Vidhya community.
Why it matters: Early-career examples make the field less abstract for students and career changers.
Current status: A current affiliation was not verified in the available record.
Learn from her: Map your first six months of learning to one small, finished project rather than an endless list of tools.
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These nine names came from Analytics Vidhya’s community-focused portion. Their inclusion is not a claim that they held the same seniority as professors or executives. It recognizes writing, projects, peer support and visible participation as legitimate forms of influence.
Pavleen Kaur — peer learning
The 2019 article highlighted Kaur as a community contributor. A current role was not verified. Follow the historical example by answering a beginner’s question with a reproducible example.
Shilpi Bhabhra — applied practice
Bhabhra was included for practical data-science participation. Current affiliation is unverified; build on the example by documenting a complete analysis from raw data to recommendation.
Divya Choudhary — community contribution
Choudhary appeared in the community list. The available sources do not establish a current title. A useful next step is to contribute a clear tutorial, review or project note.
Srishti Gupta — analytics learning
Gupta was recognized in the 2019 community group. Current status is not verified here. Recreate one public analysis and test whether its conclusions hold under a different sampling choice.
Mathangi Sri — practical data science
Sri was included as a community practitioner. No current affiliation is confirmed. Learn from this route by combining a domain problem with a small, measurable model.
Prarthana Bhat — emerging practitioner
The original page contains a spelling inconsistency, but its body uses “Prarthana Bhat.” She was included among community contributors; a current role was not verified. Keep your own professional record consistent across profiles and portfolios.
Anchal Gupta — peer support and projects
Gupta appeared in the community section. Current status is unverified. Start a study group or code review circle that produces finished work rather than passive discussion.
Preeti Agarwal — accessible analytics
Agarwal was included for community participation. The available sources do not confirm a current affiliation. Explain one analytical result without jargon and invite a reader to challenge the assumptions.
Tanvi Purohit — early-career visibility
Purohit appeared among the 2019 community contributors. Current role is not verified. Use the example as encouragement to publish progress, ask precise questions and seek feedback.
What these careers have in common
- Technical depth and communication reinforce each other: Research, products and policy all require explaining uncertainty and trade-offs.
- Data science is broad: Statistics, computer science, economics, domain expertise, design and operations can all be entry points.
- Career paths are nonlinear: The list spans universities, technology companies, startups, education and community work.
- Community is infrastructure: Teaching, mentoring, documentation and peer support expand who can participate.
- Responsible use matters: Better decisions require attention to data quality, context, fairness, privacy and consequences—not only predictive accuracy.
How to follow and learn from women in data science today
- Choose one person whose field matches your goal: research, analytics, product, education or leadership.
- Use an official biography, research page, book, paper or recorded talk as your primary source.
- Reproduce one small project or explain one concept in your own words.
- Join a community. Women in Machine Learning offers profiles, events, a community Slack and a directory; Women in Data describes its mission and professional community.
- Find a peer group, mentor or local meetup, and set a specific six-week project goal.
This approach turns a list of names into a learning plan while respecting that current jobs and affiliations change.
Frequently Asked Questions
Is this a ranking of the 29 most influential women?
No. It preserves a curated 2019 Analytics Vidhya selection and groups people by contribution. It is neither a ranking nor an exhaustive representation of women in data science.
Are all the job titles from the original article still current?
No. Many were tied to 2019 employers. This article labels those descriptions as historical and gives current first-party information only where it is verified.
What is the difference between data science, analytics and AI research?
Analytics focuses on measuring and explaining business or operational questions; data science may include experimentation, prediction and product decisions; AI research develops new methods, theory, datasets or systems. The fields overlap but are not interchangeable.
Do I need a computer-science degree to enter data science?
No single degree is required. People enter through statistics, economics, engineering, computer science, domain expertise, analytics and education. A portfolio demonstrating sound questions, data handling, evaluation and communication is more informative than a tool list.
Quick Recap
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