Value Driven Data Science

Hosted by Dr Genevieve Hayes

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Episodes

127

Latest episode

Oct 2026

Language

EN-US

About the show

Your stakeholders don’t want a better model. They want a better decision. Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce. Each week, Dr Genevieve Hayes speaks with leading thinkers and data practitioners who have already made that shift. You’ll leave every episode with an idea or framework you can start using right away. If you’re tired of being treated as just another data scientist, Value Driven Data Science will transform you into the trusted advisor your stakeholders ask for by name.

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60 recent

October 28, 2026Episode 12926 min

Episode 129: Building Digital Twins Executives Can Trust

When the cost of failure is low, experimentation is often the best way to make decisions. But when a wrong call could cost millions or endanger lives, you need an entirely different approach. This is where digital twins come in. And recent technological advances have made them accessible to far more organisations than ever before. In this episode, Dr Greg Biegel joins Dr Genevieve Hayes to explore what digital twins actually are, how they enable better executive decision-making, and what data scientists need to know to start working with them. You'll discover: What a digital twin actually is and what distinguishes it from a traditional simulation [02:35] The five capability levels of digital twins explained through Google Maps [09:12] How digital twins transform raw operational data into executive decisions [14:07] Why you should always start from a decision and not a technology [25:20] Guest Bio Dr Greg Biegel is the author of Digital Twins in Action and has been working as a researcher, software developer, architect and product manager across academia, startups and large enterprises for the past 26 years. He currently works as a Solution Architect with Amazon Web Services. Links Connect with Greg on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE

October 21, 2026Episode 12812 min

Episode 128: [Value Boost] The Three Types of Memory Every AI Agent Needs

One of the most frustrating things about working with AI is when it seems to suddenly forget everything it knew about you and your work. This is annoying when using AI chatbots. When building AI systems, it can be catastrophic. In this Value Boost episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explain what memory actually means in AI agents and what data scientists need to understand to build systems that actually remember what matters. You'll discover: The three types of memory every AI agent needs [01:54] Why context and memory are not the same thing [05:37] What context rotting is and why it matters for how you design AI systems [09:15] How to make sure LLMs pay attention to what actually matters [10:39] Guest Bio Dr Imran Ahmad is a data scientist and president of data consultancy Cloudanum. He is also the author of 30 Agents Every AI Engineer Must Build and 50 Algorithms Every Programmer Should Know . Links Connect with Imran on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE

October 14, 2026Episode 12722 min

Episode 127: From Algorithms to Agents and What It Means for Data Science

The agentic AI era hasn't made algorithms irrelevant. It has just changed where data scientists need to focus their attention. And the data scientists who understand that distinction, and can explain it to stakeholders who don't, are the ones who will define what the profession looks like next. In this episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explore the shift from algorithms to agents and what it means for how data scientists should be thinking about their role. You'll discover: How the data science toolkit has fundamentally shifted since the start of the AI era [02:09] The difference between broad AI and deep AI [10:18] The problems that still demand deep algorithmic solutions [14:10] How to push back when stakeholders insist on an LLM solution [19:21] Guest Bio Dr Imran Ahmad is a data scientist and president of data consultancy Cloudanum. He is also the author of 30 Agents Every AI Engineer Must Build and 50 Algorithms Every Programmer Should Know . Links Connect with Imran on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE

October 7, 2026Episode 12621 min

Episode 126: Why AI Excels at Some Things and Sucks at Others

Large language models excel at some tasks while struggling badly with other. At a time where it seems everyone is currently trying to make sense of AI, understanding and being able to explain exactly why this is the case, has become one of the most useful things data scientists can offer their stakeholders. In this special reverse interview episode, Lauren Pearl switches roles with Dr Genevieve Hayes to explore why LLMs work the way they do and what that means for anyone using AI in their work. You'll discover: 1. What your training data tells you about what AI can and cannot do [05:05] 2. Why high context problems are particularly hard for LLMs [06:28] 3. How AI gives you the right answer for the wrong reason [08:08] 4. Why some problems will always be beyond what LLMs can solve [14:22] Guest Bio Lauren Pearl is a business strategist, three-time founder and CFO advisor who helps start-up teams build data-driven businesses that thrive. She is also the resident start-up finance expert at NYU Stern’s Berkley Center for Entrepreneurship, where she teaches financial modelling to founders, and the co-host of The Growth-Minded CFO podcast. Links Connect with Lauren on LinkedIn Subscribe to The Daily CFO newsletter Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE

September 30, 2026Episode 12526 min

Episode 125: The AI Chicken Nugget Problem

Building something valuable with AI doesn't automatically make you valuable. If the value sits in your creation and your employer owns your creation, you're the one who's dispensable, not the thing you built. This is the AI chicken nugget problem and many data scientists who are impacted by it haven't even realised it yet. In this episode, Lauren Pearl joins Dr Genevieve Hayes to explore where real expertise still lives in the age of AI and what data scientists should actually be focusing on to build a sustainable career. You'll discover: What the AI chicken nugget problem is and why it matters [02:31] Why vibe coding is not a rare skill and never will be [05:12] Why obsessing over something rare and valuable is your best career bet [17:17] The skills worth investing in for a sustainable career [22:18] Guest Bio Lauren Pearl is a business strategist, three-time founder and CFO advisor who helps start-up teams build data-driven businesses that thrive. She is also the resident start-up finance expert at NYU Stern’s Berkley Center for Entrepreneurship, where she teaches financial modelling to founders, and the co-host of The Growth-Minded CFO podcast. Links Connect with Lauren on LinkedIn Subscribe to The Daily CFO newsletter Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE

September 23, 2026Episode 12411 min

Episode 124: [Value Boost] 8 Disciplines Every Decision System Needs

Machine learning is what most data scientists know best. But when it comes to building systems that enable better decision-making, machine learning is just one piece of the puzzle. And leaning on it too much can leave your entire decision system exposed. In this Value Boost episode, Adam DeJans Jr joins Dr Genevieve Hayes to walk through the eight disciplines that make up the modern decision stack and where data scientists should focus to close the gaps in their own skill set. You'll discover: The eight disciplines that make up the modern decision stack [01:24] The most overlooked layer in most decision systems [04:47] Why optimization textbooks are teaching data scientists the wrong things [06:17] What to learn next if you want to build better decision systems [09:45] 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 Be among the first to hear about the release of each new podcast episode by signing up HERE

September 16, 2026Episode 12325 min

Episode 123: How to Build a Decision Factory Inside Your Organisation

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 Be among the first to hear about the release of each new podcast episode by signing up HERE

September 9, 2026Episode 12211 min

Episode 122: [Value Boost] 4 Questions Every Data Scientist Should Ask About AI Strategy

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

September 2, 2026Episode 12129 min

Episode 121: What Hybrid Agentic AI Organisations Mean for Data Science

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

August 26, 2026Episode 12012 min

Episode 120: [Value Boost] The AI Silent Correctness Problem

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