Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

The traditional career path of climbing the corporate ladder no longer appeals to many data scientists - who crave freedom and ownership of their work. Yet the leap from employment to independence can feel risky and uncertain, especially without a clear roadmap for success.

In this episode, Daniel Bourke joins Dr. Genevieve Hayes to share his journey from machine learning engineer to successful independent data professional before age 30, revealing the practical steps and mindset shifts needed to transform technical skills into sustainable freedom.

In this episode, you'll discover:

The right book at the right time can completely transform your career trajectory, but many data professionals struggle to find resources that directly address their unique challenges of bridging technical expertise with business impact. While technical skills courses are abundant, guidance on becoming a strategic data leader remains scarce.

In this Value Boost episode, Kashif Zahoor joins Dr. Genevieve Hayes to reveal how he transformed his entire data team's performance and culture through a simple but powerful approach: starting a BI book club that costs almost nothing but delivers enormous ROI.

This episode reveals:

Many data scientists begin their careers expecting to influence strategic decisions, only to find themselves trapped as "data order takers" - endlessly running reports and responding to requests without understanding their business impact. This reactive approach limits career growth and earning potential, keeping even experienced professionals from reaching their strategic potential.

In this episode, Kashif Zahoor joins Dr. Genevieve Hayes to share his journey from data order taker to strategic business partner, revealing a practical framework that any data professional can use to transform their role and accelerate their career growth.

You'll learn:

Even the most compelling data presentation can fail if it runs headfirst into your stakeholders' cognitive blind spots. Decision makers who claim to be "data-driven" often unconsciously filter information through their existing beliefs, leaving brilliant insights ignored or dismissed.

In this Value Boost episode, Dr. Russell Walker joins Dr. Genevieve Hayes to reveal practical techniques for identifying and overcoming the cognitive biases that sabotage data-driven decision making.

This episode reveals:

Data storytelling might make your findings memorable, but persuasion is what gets your recommendations implemented. 

Many data scientists have mastered communication and storytelling, yet still watch their brilliant insights gather dust because they haven't learned the crucial difference between informing stakeholders and persuading them to act.

In this episode, Dr. Russell Walker joins Dr. Genevieve Hayes to reveal how battle-tested frameworks from competitive debating can bridge this gap, transforming data scientists from skilled communicators into persuasive advocates who drive real organizational change.

This conversation reveals:

Social media data drives countless business decisions, but up to 40% of social media engagement may be artificial or manipulated by bots. For data scientists accustomed to cleaning messy data, deliberately manipulated data presents an entirely different challenge that requires specialized detection techniques.

In this Value Boost episode, Tim O'Hearn joins Dr. Genevieve Hayes to reveal practical strategies for identifying and filtering out bot activity from social media datasets to extract trustworthy business insights.

This episode uncovers:

Social media algorithms silently shape what billions of people see and how they interact online. While most data scientists work to optimize business value within platform rules, there's valuable knowledge to be gained from understanding how these systems can be exploited - knowledge that can make ethical data scientists better at their jobs.

In this episode, Tim O'Hearn joins Dr. Genevieve Hayes to share insights from his experience manipulating social media platforms, revealing what ethical data scientists can learn from understanding the dark side of algorithmic systems.

This conversation reveals:

Most dashboards and reports get ignored despite all the technical expertise that goes into creating them. The reason isn't technical limitations or poor data quality - it's that they fail to deliver value to the people who are supposed to use them.

In this Value Boost episode, Nicholas Kelly joins Dr. Genevieve Hayes to reveal proven strategies for increasing dashboard adoption and showcasing your value as a data professional.

In this episode, you'll discover:

Despite unprecedented data abundance and widespread data science education, even experienced data professionals still struggle to interpret data effectively. They draw wrong conclusions, miss critical insights, or fail to communicate findings in actionable ways.

In this episode, Nicholas Kelly joins Dr. Genevieve Hayes to tackle the critical challenge of data interpretation - revealing why technical expertise alone isn't enough and sharing practical frameworks for transforming raw data into actionable business insights that drive real organisational change.

This conversation reveals:

Can you clearly articulate what makes your data science work valuable - both to yourself and to your key stakeholders? Without this clarity, you'll struggle to stay focused and convince others of your worth.

In this Value Boost episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how creating a compelling value proposition transformed his data team from report writers to strategic partners by providing both external credibility and internal direction.

This episode reveals:

Internal data science teams face a unique challenge - they're providing an invisible service that only gets noticed when something goes wrong. This puts data scientists in the awkward position of having to market themselves within their own organization, without any marketing training.

In this episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how he applied his PhD research in services marketing to transform his water utility's data team from "report writers" to strategic partners by positioning data science as "Insights-as-a-Service."

This episode explains:

When deadlines loom, it's easy for data scientists to fall into the trap of cutting corners and bending analyses to deliver what stakeholders want. But what if a simple framework could help you maintain quality under pressure while preserving your professional integrity?

In this Value Boost episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to reveal his powerful "Knowledge first, Technology second, Opinions third" hierarchy - a  framework that will transform how you handle stakeholder pressure without compromising your standards.

In this episode, you'll discover:

The data science world has always been obsessed with tools and techniques - a fixation that's only intensified in the era of generative AI. Yet even as ChatGPT and similar technologies transform the landscape, the fundamental challenge remains the same - turning technical capabilities into business results requires a process most data scientists never learned.

In this episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to discuss why the scientific process behind data science remains more critical than ever, sharing how his original "Think Like a Data Scientist" framework has evolved to harness today's powerful AI capabilities...

Even the most brilliant data analysis can fall flat when presented with poor visualisations. Many data scientists simply use default charts from their analysis software, missing the opportunity to create compelling visuals that drive understanding and decision-making.

In this Value Boost episode, Bill Shander joins Dr. Genevieve Hayes to share the design principles that can transform technical charts into powerful communication tools - even for those without formal design training.

This quick-hit episode reveals:

Data scientists can often find themselves in a frustrating cycle - meticulously executing stakeholder requests only to discover what they delivered isn't what was actually needed. The disconnect between what stakeholders ask for and what truly solves their problems can derail projects and limit advancement of your career.

In this episode, Bill Shander joins Dr. Genevieve Hayes to reveal the "Stakeholder Whispering" approach from his new book - a methodology that transforms technical experts from order-takers into strategic partners who uncover and address true business needs.

This conversation reveals:

Looking for powerful AI tools that can dramatically boost your impact, regardless of the size of the businesses you serve? 

You don't need an enterprise-size budget to transform your work and create massive value for your stakeholders.

In this Value Boost episode, Heidi Araya joins Dr Genevieve Hayes to reveal three high-impact, low-cost AI tools that deliver exceptional ROI for both your data science career and for even the most budget-conscious clients.

In this episode, you'll uncover:

While most data scientists chase after scraps at the big business table, a hidden gold mine sits completely ignored. Small businesses are desperate for AI solutions but can't get help because everyone thinks they're "too small."

The truth? These overlooked clients - representing a staggering 99.8% of all businesses - are willing to pay real money for simple AI implementations that deliver jaw-dropping ROI. We're talking five to seven-figure returns from solutions you could build in your sleep.

In this episode, Heidi Araya joins Dr Genevieve Hayes to reveal exactly how data scientists can escape the...

Would you believe that sharing a conversation in the lunch room could be more valuable to your data science career than spending countless hours behind a computer, perfecting algorithms and models? It's a radical idea, but it's exactly the kind of thinking that transforms good data scientists into exceptional ones.

In this Value Boost episode, AI strategist Gregory Lewandowski joins Dr Genevieve Hayes to explain his controversial 90-10 rule: that success in AI and data science is 90% about people and only 10% about technology - and shares a surprisingly simple way to put this principle into practice.

If you want to succeed in data science, you need to create business value. But what does business value actually mean to the executives with the power to make or break your data science initiative?

In this episode, AI strategist Gregory Lewandowski joins Dr Genevieve Hayes to share the five executive priorities he discovered while leading analytics for major enterprises - and explain why the future belongs to data scientists who understand them.

This episode reveals:

Everyone’s talking about AI, but the real opportunities for data scientists come from being in the room where key AI decisions are made.

In this Value Boost episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share a specific, proven strategy for leveraging the current AI boom and becoming your organisation’s go-to AI expert.

This episode explains:

Curiosity may have killed the cat, but for data scientists, it can open doors to leadership opportunities.

In this episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share how his habit of asking deeper questions about the business transformed him from software engineer #30 at Wayfair to a seasoned technology executive and MIT Sloan MBA candidate.

You’ll discover:

Every data scientist knows the sinking feeling: you’ve done brilliant technical work, but your presentation falls flat with stakeholders.

In this Value Boost episode, communications expert Lauren Lang and data analyst Dr Matt Hoffman join Dr Genevieve Hayes to share their go-to pre-presentation checklist to ensure that sinking feeling never happens again.

You’ll walk away knowing:

It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.

In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their expertise.

Their surprising findings about Gen AI’s impact on developer productivity went viral across 75 global media outlets – not because of complex statistics, but because of how they told the story...

Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.

Software developers have known for decades that the real key to productivity lies somewhere else.

In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques that transformed his 20-year development career – which you can use to transform your data science productivity right now.

Get ready to discover:

Why do some data scientists produce results at a rate 10X that of their peers?

Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.

In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.

This episode reveals:

Are your data science projects failing to deliver real business value?

What if the problem isn’t the technology or the organization, but your approach as a data scientist?

With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.

In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.<...

In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.

But in helping everyone else in the business, data professionals can often forget to help themselves.

However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data prof...

In the 2002 movie, Minority Report, the future of data interaction is depicted as Tom Cruise standing in front of a computer monitor and literally grabbing data points with his hands. Data interaction is shown to be as easy as interacting with physical objects in the real world.

This vision of a world where data is accessible to all was considered to be science fiction when Minority Report was first released. But over 20 years later, we are now at a point where technology has become good enough for this to soon become fact. And its data science that’s...

When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”

When Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps...

The idea of targeted marketing is nothing new. Even before the advent of computers and data science, businesses have always tried to optimise their advertising campaigns by tailoring their advertisements to their ideal buyers.

Data science allowed businesses to become more effective at this targeting. However, it was still necessary for businesses to manually create the advertising content they wanted to share with their target buyers. That is, until recently.

In this episode, Hikari Senju joins Dr Genevieve Hayes to discuss how advances in AI technology have made it possible to generate personalised advertising content...

It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.

Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.

However, it’s not the technical capabilities of data scientists that let them down – those are now better tha...

For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?

That’s where causal analytics can help. However, causal inference is rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.

In this episode, Joanne Rodrigues joins Dr G...

With all the reports about the spread of misinformation and disinformation on social media, sometimes it feels like one of the biggest threats to democracy is technology. But no technology is inherently good or bad. It’s how you use it that matters. And just as technology has the potential to harm democracy, it also has the potential to enhance it.

In this episode, Vikram Oberoi joins Dr Genevieve Hayes to discuss how he has been using generative AI and large language models (LLMs) to enhance people’s access to NYC council meetings through his work on city...

Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.

Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guessw...

As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.

But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?

In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to crea...

Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.

But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.

Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.

Yet, in building a data team, many organisations assume the only solution is to build from within. And although this may be the right solution for some organisations, building a solution isn’t right for all.

In thi...

When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.

Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).

In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to i...

For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.

Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but unable to create value from their work. However, this doesn’t necessarily have to be the case.

In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists an...

One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.

In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss the impact of the data scientist on the creation of the next generation of data orchestration tools.

Guest Bio

Sandy Ryza is a data...