Value Driven Data Science

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 your thinking with the world and the patience to let that compound over time.
In this Value Boost episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to share how selectively giving away his work for free helped him become one of the most cited and influential statisticians in the world, and what data professionals at any stage of their career can learn from that approach.
In this episode, you'll discover:
Why Rob decided to give away his work for free from the start of his career [01:42]
How open source software multiplied the impact of his research [05:58]
Why authority building is a virtuous cycle and how to start it [09:47]
Why starting small is the right move [10:35]
Guest Bio
Prof. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.
Links
Rob's website
Otexts' website
Connect with Genevieve on LinkedIn
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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 some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty.
In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it.
In this episode, you'll discover:
Why throwing data at an LLM is no substitute for building a model that understands the problem [04:27]
How combining classical statistics and machine learning can produce better forecasting results than either approach alone [08:22]
What data scientists lose when they stop thinking probabilistically - and why it matters for decision making [12:38]
Where to start if you want to strengthen your statistical foundations [25:10]
Guest Bio
Prof. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia.
Links
Rob's website
Otexts' website
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

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

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

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