Keynote: Using Data Science to Recruit Data Scientists
summary
Uses NLP and behavioral experiments on 15 million job descriptions plus candidate search logs to show concrete effects: adding senior data scientist cuts visibility and total applicants, halves fill success versus equivalent junior roles, and reduces female applicant share (confidence gap explains part, visibility gap about half). Tools include dependency parses, skills mapping, word embeddings; findings: PhD requirements fall, Hadoop demand collapses, SQL shifts by verticals; saying competitive salary deters applicants and lengthens time to fill.
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chapters (20)
- 0:00War on talent in data science
- 1:47Hiring process inconsistencies
- 3:10Job decisions by candidates
- 4:56NLP for career documents
- 6:44Smart editor for job descriptions
- 9:12Misleading job titles
- 12:45Senior data scientist myth
- 15:56Confidence and visibility gaps
- 18:45Senior titles reduce female candidates
- 20:50Hidden meaning in salaries
- 23:59Perks matter more than you think
- 26:47Rooney rule and diversity
- 29:40Large candidate pools improve outcomes
- 32:26Data science skills evolution
- 35:53Shifts in skill requirements
- 38:45Skills across industries
- 40:01Q&A: Startup job titles
- 44:13Q&A: Interview stages
- 45:36Q&A: Holistic hiring
- 49:10Q&A: AI replacing HR
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