Episode 118: [Value Boost] Compounding Your Data Science Authority Beyond Blog Posts
Value Driven Data ScienceShow Notes
A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have anticipated. In this Value Boost episode, Cynthia Dunlop joins Dr Genevieve Hayes to explore how data scientists can convert blog writing into bigger opportunities and what it actually takes to make the leap from blog post to book. You'll discover: How conference organisers actually find their speakers — and why blogging is the answer [02:35] How acquisitions editors scout for authors and why you don't need a huge following [03:39] The low risk way to find out if you're ready to write a book [06:57] How each new opportunity compounds the authority you've already built [08:10] Guest Bio Cynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack. Links Connect with Cynthia on LinkedIn Follow Cynthia on Substack Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
More from Value Driven Data Science
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
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
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
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
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