Organizational Models for Data Scientists

Organizational Models for Data Scientists

When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t positioned correctly to bring value to their organization. Maybe they don’t know what problems to work on, or they build solutions to those problems but nobody uses what they build. A lot of this can be traced back to the way the team is organized, and (relatedly) how it interacts with the rest of the organization, which is what we tackle in this issue. There are lots of options about how to organize your data science team, each of which has strengths and weaknesses, and Pardis Noorzad wrote a great blog post recently that got us talking. Relevant links: https://medium.com/swlh/models-for-integrating-data-science-teams-within-organizations-7c5afa032ebd

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Episoder(331)

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Mechanistic Interpretability: How Researchers Try To See Inside Models

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LLM As A Judge

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The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)

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21 Sep 29min

Better Know A Benchmark: ExploitGym

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When OpenAI's frontier models were caught hacking Hugging Face's servers, most people assumed they were hunting for answer keys. The real story is stranger and more unsettling. Katie and Phoebe unpack...

14 Sep 32min

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