Panel - Women in Data Science
summary
Panel summarizes concrete work and numbers: applying ML to EHR to predict two-year fracture risk using NLP-style next‑token models and active COVID outcome models, optimizing deep models for AR/facial/sound on-device (machine-learning on mobile), and building ad personalization under GDPR/CCPA and iOS14 constraints. Tools mentioned include Cognos, SPSS, Cloud Pak for Data and open-source AI Explainability 360/AI Fairness 360. Hiring data cites 3,265 survey respondents and a rise to 27 percent women in data roles; recruiting advice stresses networks, diverse interview panels and measurable equity steps.
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chapters (12)
- 0:00Introduction to panel
- 1:32Panelist introductions
- 3:58What data science means
- 9:10Top data science problems
- 16:10Career path stories
- 25:21Trends in data science
- 30:12Gender and diversity issues
- 44:22Effective allyship
- 59:47Diversity hiring strategies
- 1:11:14Work-life balance during COVID
- 1:23:34Advice for aspiring data scientists
- 1:26:19Closing remarks and Q&A
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