Value Driven Data Science

Data scientists are trained to build better models. But stakeholders don't wake up hoping for a better model. They wake up hoping to make better decisions. And the gap between those two things is where most data science value quietly disappears.
In this episode, Adam DeJans Jr joins Dr Genevieve Hayes to share what it actually looks like when an organisation stops optimising for better predictions and starts building systems that enable better decisions - that is, decision factories.
You'll discover:
What separates a decision factory from a machine learning pipeline [02:11]
Why uncertainty is an input into good decision making [10:51]
How to test a decision policy before it costs you real money [12:11]
Why giving stakeholders options beats giving them the optimal solution [18:48]
Guest Bio
Adam DeJans Jr. is a decision scientist and optimisation expert who has led high-stakes decision systems in complex, uncertain environments, at companies including Amazon, Toyota and Ford. He is the co-founder of AI and decision intelligence consultancy Bit Bros and co-author of The Decision Factory: A Novel About Decisions Under Uncertainty.
Links
Connect with Adam on LinkedIn
Bit Bros website
Connect with Genevieve on LinkedIn
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Choosing which AI tools to buy is the easy part of an AI strategy. The hard part - and the part most organisations are ignoring - is everything that sits beneath the tools, from platform and inference decisions to hardware and sovereignty.
In this Value Boost episode, Victor Coimbra joins Dr Genevieve Hayes to share his framework for cutting through the noise of AI tool selection to the strategic decisions that will actually determine whether an organisation's AI future succeeds or fails.
You'll discover:
The four layers of an agentic AI strategy and why most organisations only think about one [01:53]
The three symptoms that signal an organisation's AI strategy is breaking down [05:52]
The four questions that reveal what an organisation's AI strategy is actually missing [08:21]
The one question data scientists should lead with when advising stakeholders on AI [10:43]
Guest Bio
Victor Coimbra is a Partner and CTO at Artefact, the world’s largest pure-play AI consulting firm and co-founded the firm’s Latin American operations. In 2024, he was recognised in the Forbes 30 Under 30 Brazil list for his outstanding contributions to AI innovation.
Links
Connect with Victor on LinkedIn
Victor's article on the four-layer framework
Artefact website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

The debate about whether AI will replace human workers has already been settled - not by academics or futurists, but by the organisations that fired their humans, discovered AI couldn't do what they needed, and quietly hired them back. The future isn't AI replacing humans. It's humans and AI working together in ways that neither could manage alone.
In this episode, Victor Coimbra joins Dr Genevieve Hayes to share what hybrid agentic organisations actually look like in practice, and what data scientists need to do to position themselves at the centre of them.
You'll discover:
Why thinking of AI as a tool rather than a coworker is the mindset holding most organisations back [03:58]
The four archetypes that determine which tasks belong to humans and which to agents [08:17]
How AI is turning data scientists back into scientists [18:24]
The two skills that will define an indispensable data scientist in a hybrid organisation [27:01]
Guest Bio
Victor Coimbra is a Partner and CTO at Artefact, the world’s largest pure-play AI consulting firm and co-founded the firm’s Latin American operations. In 2024, he was recognised in the Forbes 30 Under 30 Brazil list for his outstanding contributions to AI innovation.
Links
Connect with Victor on LinkedIn
Artefact website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

AI hallucinations get all the attention. But hallucinations are relatively easy to catch because the output is obviously wrong. The failure mode that should worry data scientists more is when the agent uses facts that are true to draw conclusions that are false, producing outputs that look perfectly fine. This is known as silent correctness.
In this Value Boost episode, Jia Huang joins Dr Genevieve Hayes to explore why silent correctness is the most dangerous failure mode in agentic AI systems and what data scientists can do to catch it before it causes serious harm.
You'll discover:
Why silent correctness is harder to catch than a hallucination [04:17]
Why sampling and auditing are non-negotiable in agentic systems [07:09]
Four techniques data scientists can use to catch silent failures [09:28]
The one safeguard every agentic AI system should have [11:10]
Guest Bio
Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems.
Links
Connect with Jia on LinkedIn
Follow Jia on Substack
Agent Design Pattern Society (ADPS) website
Jia's AI agent design position paper
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work.
In this episode, Jia Huang joins Dr Genevieve Hayes to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it.
You'll discover:
Why data scientists are better prepared for the agentic AI era than they might think [03:00]
How the data scientist's role is shifting from analyst to system designer [06:37]
The three types of uncertainty in agentic AI systems [11:58]
Why context engineering is the new feature engineering [23:19]
Guest Bio
Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems.
Links
Connect with Jia on LinkedIn
Follow Jia on Substack
Agent Design Pattern Society (ADPS) website
Jia's AI agent design position paper
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE