#156 Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist

15 snips

Sep 25, 2023

In this engaging discussion, Cassie Kozyrkov, former Chief Decision Scientist at Google and now CEO of Data Scientific, shares her insights on decision-making in the age of data and AI. She emphasizes the importance of mastering decision intelligence and highlights the disparity between perceptions of data science and its realities. Cassie explains how generative AI is reshaping data scientists' roles and the crucial need for data quality. She advocates for democratizing data skills and fostering interdisciplinary collaboration to enhance decision-making processes.

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ADVICE

Data Science Interview Questions

When interviewing for data science roles, ask about data availability, quality checks, and documentation.
Inquire about whose responsibility it is to provide data and who ensures the tools work.

INSIGHT

Data Science Job Types

Data science roles typically fall into two categories: working on established teams or as a singleton in less data-mature organizations.
Each type has pros and cons regarding impact, collaboration, and project complexity.

INSIGHT

Generative AI's Impact on Data Science

Generative AI has impacted data science by acting as advanced code assistance and shifting the focus to design.
Data scientists should focus on testing and evaluating the results of generative AI for specific use cases.

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From the dawn of humanity, decisions, both big and small, have shaped our trajectory. Decisions have built civilizations, forged alliances, and even charted the course of our very evolution. And now, as data & AI become more widespread, the potential upside for better decision making is massive. Yet, like any technology, the true value of data & AI is realized by how we wield it.

We're often drawn to the allure of the latest tools and techniques, but it's crucial to remember that these tools are only as effective as the decisions we make with them. ChatGPT is only as good as the prompt you decide to feed it and what you decide to do with the output. A dashboard is only as good as the decisions that it influences. Even a data science team is only as effective as the value they deliver to the organization.

So in this vast landscape of data and AI, how can we master the art of better decision making? How can we bridge data & AI with better decision intelligence?

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 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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