Cassie Kozyrkov

Kozyr

Speaking and advisory services. Request form: makecassietalk.com

Kozyr was renamed from Data Scientific.

Chief Executive Officer

Federal Reserve Bank of New York

Served a full two year term as Innovation Advisor and Councillor at the Federal Reserve Bank of New York, with a focus on the impact of AI on the financial markets and global policy.

Innovation Advisory Council Member

Google

Decision Intelligence is an interdisciplinary field concerned with all aspects of decision-making.  It combines Data Science (statistics, machine learning, AI, analytics) with the Behavioral Sciences (psychology, neuroscience, economics, and the managerial sciences).

I serve as a thought leader in this new discipline, guiding Google and the world in making safe, effective use of AI and data.

Chief Decision Scientist

My mission is to democratize artificial intelligence and guide business leaders in harnessing the transformative power of machine learning.

Chief Data Scientist, Office of the CTO

Youngest ever member of Google's Office of the CTO, specializing in AI and data science.

I'm also a coauthor of Google's Technical Director training program to teach others how to succeed in the role.

Technical Director, AI and Data

My roles are: statistician, machine learning expert, mentor, data scientist, evangelist, decision scientist, and consultant.  I bridge the gap between leaders and quantitative analysts / researchers.

As Google's statistics and machine learning sensei:
* I've advised 200+ teams at Google on their analytics processes.
* My ideas have sparked 400+ new projects.
* I've trained 20,000+ analysts/leaders/engineers in rigorous data-driven decision-making and/or using machine learning to transform their work.

Largest projects in: Search Advertising, Google Maps, Machine Intelligence, Infrastructure, YouTube

Statistician

Self-Employed

Data science consulting and analytics services for clients from government organizations, private sector firms, and academia.

Data Science Consultant

North Carolina State University

Statistics Lecturer

Statistical Computing, Advanced Topics in Statistics: Big Data, Special Topics in Statistics: Data Mining, Applied Time Series Analysis, Applied Multivariate Data Analysis, Advanced Probability and Measure Theory, SAMSI Computational Advertising, Statistical Methods II, Statistical Consulting, Statistical Theory I, Statistical Theory II, Linear Models and Variance Components, Stochastic Processes, Doctoral Research, Supervised Doctoral Teaching, R Programming

Mathematical Statistics PhD Student

Duke University

Psychology & neuroscience with specialization in transcranial magnetic stimulation (TMS) methods and the science of decision-making.

Neuroeconomics PhD Candidate

Center for Cognitive and Social Neuroscience at the University of Chicago

Coordinated lab research projects and team effort. Designed and ran experiments.

Project Manager

Charitable contributions and decision-making research with Professors Steve Levitt and John List.

Research Assistant - Prof. John List

Cassie is Google’s former Chief Decision Scientist and a sought-after AI advisor, working with senior leaders at places like Lenovo, NASA, and Gucci. She is currently CEO of Data Scientific and is known for founding the field of Decision Intelligence.

Cassie is a beloved personality in the data and AI community, followed by over half a million tech professionals. She is also a renowned keynote speaker and writer. ( If you'd like her to speak at your event, here's her booking form: makecassietalk.com )

In almost 10 years at Google, 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. Before that, she served in Google's Office of the CTO as Chief Data Scientist. The rest of her 20 years of experience was split between data science, consulting, lecturing, and academia.

If you've had a good chuckle while learning about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. She has been honored as a LinkedIn Top Voice for 5 years and as a #1 Artificial Intelligence writer on Medium for 7 years.

Speaking and advisory services. Request form: makecassietalk.com

86% of enterprises plan to invest more in agentic AI.
6% trust agents to run core processes.

Let's talk about that gap!

Harvard Business Review just released some findings about tech decision-makers (n=603):
🤖 86% expect agentic AI investment to grow in 2 years
🤖 6% fully trust agents with end-to-end processes
🤖 ~25% say they're "ready" on infrastructure, data, security, and governance

That lack of trust is a good thing, in my opinion. Trusting complex probabilitistic systems without re-archtiecting complex processes is a recipe for kablooies. If anything it should give you hope. The trouble is that the redesign is difficult except in narrow use cases.

The model isn't the bottleneck anymore.
The plumbing and the people are.

Fragmented apps and data → agents can't get reliable context 
Legacy processes → never designed for autonomous execution 
Security and compliance → no playbook for "agents at the core" 
Employees → unclear if agents are tools or replacements

The report points to enterprise orchestration as the missing layer—connecting systems, standardizing actions, enforcing governance so agents behave predictably instead of becoming shadow IT.

Old question: "Which LLM should we use?"
New questions:
Which processes are we willing to redesign around agents?
How do we orchestrate data, tools, and guardrails so we can trust them with real work?

In 3 years, the gap won't be "who has agents."
It'll be who rebuilt their processes so agents can actually run them.

Where is your team right now?
A) Still experimenting at the edges (help desks, copilots) 
B) Re-architecting workflows so agents own real outcomes 
C) Somewhere in between, trying to figure out where to start
D) Waiting and watching others stub their toes

Drop your letter below. Your comments make my (digital) world 'round.

P.S. On a completely different note,  my Decision-Making with ChatGPT course is running on demand right now with a live Q&A with me next week. Come ask me anything! decisiongptcourse.com

CEO, Google's first Chief Decision Scientist, AI Adviser, Decision Strategist, Keynote Speaker (makecassietalk.com), LinkedIn Top Voice

Agentic HBR

Been wondering when my next cohort will be? Surprise! The time is NOW. If you missed the previous run or have been waiting to join, enrollment is now live:

 👉 https://lnkd.in/eG7EBtTv

The last cohorts made something clear: leaders aren’t looking for more information. They’re looking for clearer judgment. ChatGPT helps - but only when you use it in the right parts of the thinking process.

In this cohort, we’ll continue the work of turning AI into a genuine thinking partner instead of a shortcut. You’ll learn how to:

• Use ChatGPT to reduce effort without losing rigor
• Avoid the trap that turns “AI-assisted” into lazy thinking
 • Spot cognitive biases before they distort outcomes
 • Separate luck from decision quality
 • Build a decision scribe so you finally learn from your choices
 • Operate with confident judgment instead of drowning in advice

You’ll get 30+ on-demand videos (available as soon as you enroll), a 2-hour live Q&A with me (at 1 PM ET Dec 18), prerecorded Q&A from earlier cohorts, templates, frameworks, and a global community.

If you're working on sharpening your thinking in an AI world, Cohort 3 is now open.
 👉 https://lnkd.in/e-RaxVJJ

If this resonates, a repost or subscribing to decision.substack.com helps the course reach the leaders who need it.

#DecisionIntelligence #AILeadership #StrategicThinking #DecisionMaking #AI

SEEK

Data Engineer

- Handling data pipelines, data visualizations with Power BI, NLP along with GenAI in the Azure data ecosystem.
- Rewrote a pipeline for text classification using GenAI fully in Python, to speed up reiteration and improve customizability by up to 50% compared to the existing Alteryx pipelines

Data Analyst

Involve Asia

- Performing ETL for data processing and data cleaning
- Using PySpark (Python version of Spark) to process and analyze data storing in AWS S3
- Preparing reports and dashboards (in Google Sheet and Google Data Studio) according to business requirements
- Collaborate with other departments to resolve their data-related requests and also KPI reports
- Additionally, setting up and handling all the tracking and data related to Google Tag Manager (GTM) and also Google Analytics (GA), from the beginning until reports

Universiti Teknologi Malaysia

Doing research on AI for computer vision, specifically for classify calf postures, and also to estimate their weights from images as the main type of dataset.

Masters Student

Selangor Human Resource Development Centre

I am one of the main contributors to a large project being developed in the company. The application developed for the project is known as Integrated Vision Inspection System (IVIS). This app provides a user-friendly GUI for the user to conduct end-to-end machine learning pipelines primarily for computer vision tasks, including image classification, object detection, and semantic segmentation. The pipelines involve all the tasks of image collection, labeling, AI model training, and production deployment.

Machine Learning Intern

As a Data Analyst with over 2 years of working experience, I perform ETL, data processing, and data analysis using PySpark and AWS, and prepare reports and dashboards using Google Sheet and Google Data Studio. I also handle all the tracking and data related to Google Tag Manager and Google Analytics, from the setup to the reporting. I collaborate with other departments to resolve their data-related requests and KPI reports. 

I have a strong passion for Data Science and AI, which motivated me to pursue a Master of Philosophy in AI with Computer Vision at Universiti Teknologi Malaysia. I graduated in December 2022 with a research project on classifying calf postures and estimating their weights from images. I also completed the Deep Learning Specialization and the Google Data Analytics Certificate courses on Coursera to enhance my skills and knowledge. Additionally, I contributed to a large project at Selangor Human Resource Development Centre as a Machine Learning Intern, developing an AI application for industrial computer vision tasks. I am eager to keep on learning and applying Data Science and AI to solve real-world problems and help the world as much as possible.

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Cassie Kozyrkov

Anson Tan Chen Tung

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The Battle

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Anson Tan Chen Tung

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Cassie Kozyrkov's LinkedIn Performance

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