Value Driven Data Science

In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint.In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts.In this episode, you'll learn:Why a single observation reveals more than you think [01:58]How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25]The Rule of Five and what it reveals about small sample statistics [12:08]The simplest and most overlooked technique for reducing measurement uncertainty [14:07]Guest BioDouglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It.LinksHow to Measure Anything websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Making the leap from academia to industry isn't just another career change - it involves a complete shift in the way you work. Data scientists transitioning from academia face a brutal learning curve that can leave them feeling unprepared despite years of advanced training.In this episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to share her recent journey from a tenure-track academic position to working as a data scientist in industry, revealing the challenges she faced and the strategies that helped her navigate this difficult transition.You'll discover:Why academic training can leave you unprepared for industry expectations [10:49]The mindset shifts required when moving from research to business [07:50]Strategies to help bridge the gap between academic and business work [15:23]The one thing academics should do before leaving for industry [22:11]Guest BioDr Sayli Javadekar is a data scientist at Thoughtworks, with experience at the World Bank and UNAIDS. Before this, she was an Assistant Professor at the University of Bath and holds a PhD in Econometrics from the University of Geneva.LinksConnect with Sayli on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When most data scientists think about using LLMs and generative AI, the first thing that springs to mind is writing code faster. While that's certainly useful, if it's the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a data scientist.In this episode, Colin Priest joins Dr. Genevieve Hayes to explore advanced LLM applications that go far beyond code generation, including techniques for processing unstructured data, improving stakeholder communication, and identifying blind spots in your analysis.You'll learn:How to use LLMs to extract structured insights from messy unstructured data [02:50]The role-playing technique that helps you practice difficult stakeholder conversations [14:12]Why using multiple LLMs helps reduce AI hallucinations [20:38]A step-by-step approach for integrating LLMs into your workflow safely [25:52]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

There was once a time, when data science was still in its infancy, when demonstrating any attempt to learn Python or machine learning was enough to secure a job interview. The demand for data scientists massively outweighed supply. Ten years later, however, the job market has dramatically shifted - and many data scientists who unexpectedly find themselves out of work face a truly overwhelming experience.In this episode, Sarah Burnett joins Dr. Genevieve Hayes to share how she transformed redundancy from a senior banking role into the launch of her own successful data consultancy, proving that unexpected job loss doesn't have to mean career disaster.In this episode, we explore:Why redundancy is a numbers game, not personal failure [03:54]The power of taking time to process after job loss, instead of rushing back [08:47]How to pivot when your first business idea doesn't work [16:58]Why building side projects and community involvement create career insurance [20:52]Guest BioSarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.LinksConnect with Sarah on LinkedInDub Dub Data WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE