Value Driven Data Science

Building genuine expertise takes years. And in the age of AI, losing it can happen gradually and almost invisibly, through small delegations that each seem reasonable in isolation but add up to something significant over time. In this episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use AI without compromising their hard-earned expertise and reputation. You'll dis...

AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe. In this Value Boost episode, Derek Gibson joins Dr Genevieve Hayes to explore how data professionals can help their s...

AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-stakes decisions, the ability to spot what's wrong, before it reaches a stakeholder, is becoming one of the most important skills a data professional can have. In this episode, Dere...

In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint. In this Value Boost episode, Douglas Hubbard joins Dr Genevi...

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. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for handling them. In this episode, Douglas Hubbard joins Dr Genevieve Hayes to share how combining techniq...

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 them valuable in the first place. In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice. In this episode, you'll...

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 answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a virtual team of over 100 AI specialists to dramatically expand what he is able to achieve. In this epis...

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 always the right answer.  In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't the answer, how to recognise them, and how to have the conversation with stakeholders who are convinc...

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 data scientists trying to get AI projects off the ground, understanding why that gap exists is as important as the technical work itself. In this episode, Santosh Kaveti joins Dr Genevieve Hayes to share what organisations consisten...

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 factor. And the more expertise you bring, the further AI can take you. In this Value Boost episode, Brent Dykes joins Dr Genevieve Hayes to apply his Four Zones of AI Productivity framework to the insight generation process and expl...

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 interesting" - before your work disappears into a drawer, never to be seen again. The problem usually isn't the analysis. It's that interesting observations and genuine insights are not the same thing. In this episode, Brent Dykes j...

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 m...

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 statistica...

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 milest...

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 technique...

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. Ge...

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 j...

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 epi...

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 lesson...

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 cricke...

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-...

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 academia face a brutal learning curve that can leave them feeling unprepared despite years of advanced training. In this episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to share her recent journey from a tenure-track academic posit...

Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication to learning how to work with resistance rather than against it, hobbies and interests often teach lessons that directly translate to professional effectiveness. In this Value Boost episode, Colin Priest joins Dr. Genevieve Hayes to ex...

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 the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a data scientist. In this episode, Colin Priest joins Dr. Genevieve Hayes to explore advanced LLM applica...

LinkedIn has become a powerful career tool for data scientists willing to invest the time. Regular posting can lead to unexpected work opportunities, reconnections with former colleagues, and valuable networking with professionals worldwide. But making the leap from occasional posting to consistent content creation can feel overwhelming. In this Value Boost episode, Sarah Burnett joins Dr. Genevie...