Genevieve Hayes
Appears in 109 Episodes
Episode 109: How to Measure Anything and Make Better Decisions
Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them...
Episode 108: [Value Boost] How to Use AI Without Losing Your Edge
AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes the...
Episode 107: Building a Virtual Empire of AI Specialists
The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answe...
Episode 106: [Value Boost] When AI Isn't the Answer
These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't alwa...
Episode 105: From AI Idea to Production Reality
Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value.For ...
Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists
AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding...
Episode 103: The Art of the Actionable Insight
Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's...
Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist
Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share you...
Episode 101: Why Traditional Statistics Still Matters in the Age of AI
Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving som...
Episode 100: What Data Science Value Really Means
Over 100 episodes of conversations with world-class practitioners, a few ideas keep surfacing. Technical skill is necessary but never sufficient. The most valuable dat...
Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem
Biased machine learning models don't just produce poor predictions. They can damage reputations, derail projects, and in high-stakes fields like healthcare, potentiall...
Episode 98: Building Trust in AI Through Model Interpretability
When your machine learning model makes a decision that affects someone's medical treatment, financial security, or legal rights, "the algorithm said so" isn't good eno...
Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success
Data scientists often jump straight to machine learning when tackling a new problem. But there's a foundational step that can dramatically increase your chances of pro...
Episode 96: Making Better Decisions with ML and Optimisation
Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many ...
Episode 95: [Value Boost] Building Models That Work While Millions Are Watching
Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else ...
Episode 94: Creating Global Impact with Data Science
For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organis...
Episode 93: [Value Boost] What Industry Data Scientists Can Learn from Academic Training
While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable sk...
Episode 92: Making the Academia to Industry Leap in Data Science
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 acade...
Episode 91: [Value Boost] How Your Hobbies Can Supercharge Your Data Science Career
Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication t...
Episode 90: Using LLMs to Become a More Effective Data Scientist
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...