2025-11-08 –, Talk Track 1
Women make up only 22% of data and AI roles and contribute just 3% of Python commits, leaving a “missing 78%” of untapped talent and perspective. This talk shares what happened when our community doubled overnight, revealing hidden demand for inclusive spaces in scientific Python.
We’ll present the data behind this growth, examine systemic barriers, and introduce the VIM framework (Visibility–Invitation–Mechanism) — a research-backed model for building resilient, inclusive communities. Attendees will leave with practical, reproducible strategies to grow engagement, improve retention, and ensure that the future of AI and Python is shaped by all voices, not just the few.
Women represent only 22% of data and AI roles and contribute just 3% of Python commits, leaving a “missing 78%” of untapped talent and perspective. This imbalance isn’t just a diversity issue — it risks embedding bias into the foundations of AI and scientific Python.
In this talk, I share what happened when our community doubled overnight, a case study that uncovered hidden demand for more inclusive spaces in open source. Using a mixed-methods approach — combining membership data, demographic surveys, and qualitative feedback — we examine what drives rapid growth and, more importantly, what sustains it.
The core of the talk introduces the VIM framework (Visibility–Invitation–Mechanism), a research-backed model for building inclusive and resilient communities. We’ll explore how visibility (who gets seen), invitation (who feels welcome), and mechanism (what support exists) can be applied in practical, repeatable ways to unlock hidden demand and improve retention.
Attendees will learn:
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Why inclusive design is critical for sustainable growth in AI/Python communities
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The data-driven lessons from a 179% membership surge in one day
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Practical, actionable strategies to apply the VIM framework in their own projects
This session combines research and practice to provide attendees with both insight and a toolkit for building stronger, more inclusive communities.
No previous knowledge expected
Noor Aftab is the Global Program Lead at Amazon Web Services (AWS), where she drives strategic programs for Amazon S3, supporting some of the world’s most complex data, AI, and analytics workloads. With a foundation in software engineering and data science, she brings over a decade of experience building and scaling cloud-native solutions, AI/ML systems, and developer-focused programs.
She serves as Vice President of the Society of Women Engineers (SWE) Pacific Northwest section, championing technical leadership and mentoring initiatives across engineering communities. Noor is also Chair of the NumFOCUS Code of Conduct Working Group and User Group Leader for IBM Women in AI, where she fosters inclusive, resilient communities across 300+ open-source projects.
A frequent keynote speaker, Noor has presented at PyData Global, SciPy, ODSC, TEDx, IEEE, and 13+ global venues, delivering talks that connect technical depth with real-world adoption of AI and cloud. She has authored and led initiatives such as the IEEE Hour of Power AI training program, empowering engineers and professionals with practical AI skills.
Her contributions to technology and leadership have been recognized with awards, including the Australia Alumni Excellence Award and Asia Pacific HRM Congress Award, with media features in the BBC, Martha’s Vineyard Times, and Hindustan Times.
GitHub: aftabn81
| Website: www.nooraftab.com