Value Driven Data Science

Dr Genevieve Hayes
  • 5.0(6개의 평가)
  • 과학 기술
  • 매주 업데이트

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.

  1. 3월 25일

    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 WebsiteConnect with Serg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    11분

  2. 3월 18일

    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 WebsiteConnect with Serg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    25분

  3. 3월 11일

    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 project success and create more reliable business value. Mathematical modelling from first principles provides a low-cost scaffolding that can make your machine learning work more robust. In this Value Boost episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how building models from physics principles, like mass and energy conservation, creates a modular foundation that reduces computational costs and makes your work easier to understand. In this episode, we explore:1. What mathematical modelling from first principles actually means [01:20]2. How to build modular models with different resolution levels [04:39]3. When to add machine learning to first principles models [08:18]4. The practical first step to incorporate this approach into your work [09:23] Guest Bio Dr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course. Links Bluebird Optimization WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    11분

  4. 3월 4일

    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 data scientists are unaware of - that can be used to dramatically boost the business value of your ML outputs. This second layer transforms predictions into optimal decisions, and it's where the real impact often happens. In this episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how combining machine learning with decision optimisation creates solutions that go far beyond prediction, helping stakeholders make better decisions in uncertain environments. You'll discover: How decision optimisation differs from ML parameter tuning [02:19]Why combining predictions with optimisation multiplies value [13:36]The mindset shift needed to think in optimisation terms [22:59]How to spot immediate optimisation opportunities in your work [23:42]Guest Bio Dr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course. Links Get Tim's 3 Step Guide to Add Optimisation to Your Data Science SkillsBluebird Optimization WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    26분

  5. 2월 25일

    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 entirely. There's no room for the kind of errors that might be acceptable in research settings or even standard business applications. In this Value Boost episode, Prof. Steve Stern joins Dr. Genevieve Hayes to share practical lessons from deploying the Duckworth-Lewis-Stern method in high-pressure, real-time environments where mistakes have global consequences. You'll learn: Why model simplicity matters more than you think [02:04]The two types of errors you need to understand [03:21]How to test models for extreme situations [05:50]The balance between confidence and humility [07:37] Guest Bio Prof. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system. Links Contact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    12분

  6. 2월 18일

    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 organisation, but seeing them become industry standards or influence global decisions feels completely out of reach. In this episode, Prof. Steve Stern joins Dr Genevieve Hayes to share how he transformed a mathematical critique of a cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia. This episode reveals: How a single email response changed everything [05:24]Why principles build trust where mathematics can't [13:19]The "error whack-a-mole" problem that destroys credibility [16:00]The real secret to creating work with impact [30:29]Guest Bio Prof. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system. Links Contact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

    35분

  7. 2025. 12. 17.

    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 skills that many industry-trained data scientists never develop. In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths. You'll learn: The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest Bio Dr 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. Links Connect 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

    10분

  8. 2025. 12. 10.

    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 skills that many industry-trained data scientists never develop. In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths. You'll learn: The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest Bio Dr 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. Links Connect 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

    24분
모두 보기(99개)

5
최고 5점
6개의 평가

소개

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.

정보

  • 제작진
    Dr Genevieve Hayes
  • 방송 연도
    2022년 - 2026년
  • 에피소드
    99
  • 등급
    전체 연령 사용가
  • 저작권
    © 2026 Genevieve Hayes Consulting
  • 웹사이트 보기
    Value Driven Data Science

좋아할 만한 다른 항목