INTERVIEW
summary
Claims that predictive work has moved from aggregates to individual-level targeting as cheap compute and streaming data make per-customer inference practical, warns teams still applying models without domain grounding, and predicts domain-specific AI/ML will dominate. Notes a gender shortfall tied to pipeline not preference and rising female enrollment. Recommends concrete skills: Keras and cloud stacks, plus a big-data toolkit like Hadoop Cassandra Kubernetes, and preserving Python/R foundations while embracing new tools.
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chapters (4)
- 0:00Start
- 0:09TELL US ABOUT YOUR ROLE AT TEG ANALYTICS
- 4:30AS A WOMAN IN DATA SCIENCE, DO YOU THINK THAT THE GENDER GAP IN THE INDUSTRY HAS STARTED TO CLOSE
- 6:43DATA SCIENCE TOOLS WOULD ONE NEED IN 2019
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