Genevieve Hayes
Appears in 100 Episodes
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
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 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...
Episode 89: [Value Boost] LinkedIn Strategies for Boosting Your Data Science Career
LinkedIn has become a powerful career tool for data scientists willing to invest the time. Regular posting can lead to unexpected work opportunities, reconnections wit...
Episode 88: Building a Data Science Career After Unexpected Job Loss
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 interv...
Episode 87: [Value Boost] How Your Weirdness Could Be Your Data Science Superpower
When most data scientists think about their competitive edge, they focus solely on what goes on their resume - education, work experience, and technical skills. But wh...
Episode 86: Why Every Data Scientist Is Already Running a Business
Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most d...
Episode 85: [Value Boost] The Office Politics Survival Guide for Data Science Experiments
Here's something that data science courses don't prepare you for: even your most brilliant analysis can fail if you can't navigate the human side of your organisation....
Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics
When working with data, it can be easy to fall into the trap of believing that your dataset represents nothing more than numbers on a page. However, behind every data ...
Episode 83: [Value Boost] How to Gamify Data Science Requirements Gathering for Better Results
Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other...
Episode 82: Why You Should Start Your Data Projects with Pictures Not Data
Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicat...
Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist
Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Witho...