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Value Driven Data Science

Dr Genevieve Hayes

Value Driven Data Science is a masterclass where data professionals learn how to become strategic experts.

Each week, Dr Genevieve Hayes speaks with world-class data practitioners who have mastered strategic positioning, built genuine authority, and transformed their expertise into organisational influence. You'll learn how they create value by helping stakeholders make better decisions and solve real business problems with data - not just by running analyses.

If you're a data professional ready to stop being a technical executor and become a strategic expert, this masterclass is for you.

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  • 23 episodes
  • weekly
  • Avg 21 min
  • English

Episodes 23

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  • #100
    April 8 · 38 min

    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 data professionals aren't the ones who build the best models - they're the ones who know which problems are worth solving. And the gap between those two things is where most data scientists are leaving value on the table. In this milestone episode, Dr. Genevieve Hayes reflects on her career journey and the conversations that helped her arrive at these conclusions, with Matt O'Mara turning the tables to put her in the hot seat. In this episode, you'll discover: From statistician to machine learning advocate and back again - and what that journey revealed [09:49] The crack in the data science skills market where significant value is hiding [18:59] Why knowing which problems to solve matters more than knowing how to solve them [24:53] The top three lessons from 100 conversations on what data science value actually means [33:49] Guest Bio Matt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status. Links Connect with Matt 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

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  • #99
    March 25 · 10 min

    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, potentially cause real harm. Yet many data scientists don't check for bias until it's too late, missing the opportunity to address it at its source. In this Value Boost episode, Serg Masis joins Dr. Genevieve Hayes to share practical techniques for detecting and mitigating bias in machine learning models before they become major problems for you and your stakeholders. You'll discover: The most common bias patterns to watch for [01:32] How to diagnose whether bias exists in your model [04:44] The three levels where bias can be addressed [07:13] Where to intervene for maximum impact [08:17] Guest Bio Serg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python. Links Serg's Website Connect with Serg 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

    • Transcript
  • #98
    March 18 · 24 min

    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 enough. Stakeholders need to understand why models make the decisions they do, and in high-stakes environments, model interpretability becomes the difference between AI adoption and AI rejection. In this episode, Serg Masis joins Dr. Genevieve Hayes to share practical strategies for building interpretable machine learning models that earn stakeholder trust and accelerate AI adoption within your organisation. You'll learn: The crucial distinction between interpretable and explainable models [07:06] Why feature engineering matters more than algorithm choice [14:56] How to use models to improve your data quality [17:59] The underrated technique that builds stakeholder trust [21:20] Guest Bio Serg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python. Links Serg's Website Connect with Serg 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

    • Transcript
Showing 21–23 of 23 episodes