What is Decision Science? (with Cassie Kozyrkov, Google's First Chief Decision Scientist)

Cassie Kozyrkov founded the field of Decision Intelligence at Google where, until recently, she served as Chief Decision Scientist, advising leadership on decision process, AI strategy, and building data-driven organizations. Upon leaving Google, Cassie started her own company of which she is the CEO, Data Scientific. In almost 10 years at the company, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Cassie also previously served in Google's Office of the CTO as Chief Data Scientist, and the rest of her 20 years of experience was split between consulting, data science, lecturing, and academia.
Cassie is a top keynote speaker and a beloved personality in the data leadership community, followed by over half a million tech professionals. If you've ever went on a reading spree about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers.
In the full episode, Cassie and Richie explore misconceptions around data science, stereotypes associated with being a data scientist, what the reality of working in data science is, advice for those starting their career in data science, and the challenges of being a data ‘jack-of-all-trades’.
Cassie also shares what decision-science and decision intelligence are, what questions to ask future employers in any data science interview, the importance of collaboration between decision-makers and domain experts, the differences between data science models and their real-world implementations, the pros and cons of generative AI in data science, and much more.
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[@jjbuzz9230]

I've always disliked doing scientific experiments, not long ago I found a very established advertising executive that echo my thoughts, experiments are too narrowly focused on very specific circumstances to be applied to real life, complex scenarios. I'm pleasantly surprised that a very established data/decision scientist, also found the experiments pointless because the "problem" Defined, is not something that is worth exploring, AND the hypothesis, entire project strictly follows a very narrowly focused process. This is precisely why I was never motivated, interested to do experiments, it was simply a waste of time that trained our thought process to be in a certain way(very time consuming and uneffective way), completing narrowly focused experiments didn't offer any inspiration or solution to real life problems

[@Phronesis1037]

It's not exactly a without a purpose. The goal in these experiments and projects is to train our brains into a specific way of thinking, introducing a different way of thinking to our brains.

[@jjbuzz9230]

@@Phronesis1037 we should be open minded, proactively think in different ways, not in one specific, very narrow way of thinking. i've encountered doctors, dentists who follow this way of thinking, engrained since their school days, they have become very stubborn, unwilling to accept other people's reality, making serious mistakes. some of them can't even understand common sense. turning smart people into dumb ones...

[@brodyalden]

Some very interesting concepts in this. Framing is much more important than it’s generally credited for being.

[@brodyalden]

Thanks for these uploads.