Value Driven Data Science

Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their career development, putting them on the right projects and ensuring they get proper training. The reality is that while bosses usually mean well, they have their own careers to worry about.

In this episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore how applying a solo business mindset to your data science career can help you take control of your professional destiny, increase your value within organisations, and create opportunities that others miss.

You'll learn:

1. How to become the go-to person for specific problems within your organisation [07:11]
2. The "secondary sale" technique that gets your projects approved even when you're not in the room [14:49]
3. Why focusing on one shiny object at a time accelerates your career faster than juggling multiple priorities [19:06]
4. How to find your signature service that makes you indispensable to your employer [23:00]

Guest Bio

Danny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.

Links

• Connect with Danny on LinkedIn
• Download the One Shiny Object framework
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Here's something that data science courses don't prepare you for: even your most brilliant analysis can fail if you can't navigate the human side of your organisation. And office politics becomes especially tricky when you're running experiments. You're essentially asking people to place bets on their ideas - and then potentially delivering the news that their bet didn't "win".

In this Value Boost episode, Miguel Curiel joins Dr. Genevieve Hayes to share practical strategies for handling the political challenges that come with experimentation and data science work, so you can drive real change without creating enemies.

You'll learn:

1. Why running experiments is politically riskier than regular analysis [01:50]
2. The mindset shift that turns experiment "failures" into wins [03:56]
3. How to overcome the "it worked for Netflix" objection [05:07]
4. The simple strategy for reducing political friction around data work [08:24]

Guest Bio

Miguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.

Links

• Connect with Miguel 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 working with data, it can be easy to fall into the trap of believing that your dataset represents nothing more than numbers on a page. However, behind every data point is a human story - people clicking through websites, abandoning shopping carts, or binge-watching Netflix shows. 

And in our app-driven world, understanding these human behaviours has become absolutely critical - for businesses to flourish and for data scientists to have a meaningful impact in the work they do. This is where product analytics comes in.

In this episode, Miguel Curiel joins Dr. Genevieve Hayes to share his practical checklist for maximising business impact through product analytics, drawing from his own experiences analysing how people actually interact with digital products and his upcoming book on the topic.

This episode explores:

1. What product analytics actually involves, beyond just measuring clicks and conversions [03:11]
2. Why behavioural science models are crucial for understanding user motivations [07:25]
3. Miguel's seven-step checklist for building impactful product analytics capabilities [15:49]
4. The most valuable skill for data scientists in product analytics [22:27]

Guest Bio

Miguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.

Links

• Connect with Miguel 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

Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other and senior executives dominate the conversation. Yet without proper requirements, data science projects are doomed to fail due to solving the wrong problems or missing critical business needs.

In this Value Boost episode, David Cohen joins Dr. Genevieve Hayes to reveal how gamification can transform stakeholder meetings from painful obligation into collaborative problem-solving sessions that actually produce useful requirements.

You'll learn:

1. Why gamification works as a "Trojan horse" for productive business conversations [03:26]
2. How to ensure every voice is heard, not just the loudest or most senior person in the room [06:34]
3. The simple technique that prevents senior executives from dominating and skewing requirements [06:59]
4. The easiest way to add interactive elements to your next stakeholder meeting without complex games [08:20]

Guest Bio

David Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.

Links

• Connect with David on LinkedIn
• Superposition website
• Superposition YouTube channel
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us? 

In this episode, David Cohen joins Dr. Genevieve Hayes to reveal how flipping the script on data visualisation - moving it to the beginning of projects rather than the end - can dramatically improve stakeholder buy-in and project success rates.

This episode reveals:

1. Why the traditional bottom-up data communication approach often misses the mark [02:36]
2. How moving visual storytelling to the start of a project can transform stakeholder engagement [06:40]
3. The gamified workshop framework that turns requirement gathering into collaborative problem-solving [08:50]
4. The counterintuitive first step that immediately improves data project outcomes [20:28]

Guest Bio

David Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.

Links

• Connect with David on LinkedIn
• Superposition website
• Superposition YouTube channel
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the wrong problems or building models that don't address real business decisions.

In this Value Boost episode, Professor Jeff Camm joins Dr. Genevieve Hayes to reveal the specific problem framing framework that decision scientists use to ensure they're solving the right problems from the start, dramatically improving their success rates compared to traditional data science approaches.

You'll discover:

1. The medical doctor approach to diagnosing business problems by distinguishing symptoms from root causes [02:09]
2. The critical question that reveals what decisions actually need to be made [04:53]
3. How to turn model "failures" into valuable strategic insights for management [06:24]
4. Why thinking beyond the data prevents you from building technically perfect but business-useless solutions [10:04]

Guest Bio

Prof Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.

Links

• Connect with Jeff 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

Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial difference that dramatically improves success rates.

In this episode, Prof Jeff Camm joins Dr. Genevieve Hayes to explore how decision science approaches problems differently from data science, why decision science approaches lead to higher success rates, and how data scientists can integrate these techniques into their own work.

This episode reveals:

1. The fundamental difference between modelling data and modelling decisions [04:12]
2. Why decision science projects have historically had higher success rates than current data science efforts [10:42]
3. How to avoid the "ill-defined problem" trap that kills most data science projects [21:12]
4. The medical doctor approach to understanding what business problems really need solving [22:28]

Guest Bio

Prof Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.

Links

• Connect with Jeff 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

One of the biggest risks for independent data professionals is spending months or years developing a product or service that nobody wants to buy. The graveyard of failed data science projects is filled with technically brilliant solutions that solved problems no one actually had, leaving their creators with empty bank accounts and bruised egos.

In this Value Boost episode, Daniel Bourke joins Dr. Genevieve Hayes to reveal practical strategies for validating data product ideas before investing significant development time, drawing from his experience creating machine learning courses with over 250,000 students and building the Nutrify food education app.

This episode uncovers:

1. How to spot genuine market demand before building anything [04:15]
2. The validation strategy that guarantees you win regardless of commercial success [10:16]
3. Why passion projects often create unexpected business opportunities [06:33]
4. The simple approach that turns failed experiments into stepping stones for success [11:50]

Guest Bio

Daniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.

Links

• Daniel's website
• Daniel's YouTube channel
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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:

1. Why embracing the "permissionless economy" is crucial for independent success [14:59]
2. The power of "starting the job before you have it" [12:17]
3. Why building your own website is the foundation for long-term independent success [24:35]
4. A practical approach to opportunity selection that accelerates career momentum [17:27]

Guest Bio

Daniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.

Links

• Daniel's website
• Daniel's YouTube channel
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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:

1. How a weekly team book club transformed Kashif's data team [02:26]
2. The "data concierge" concept that transforms dashboard builders into trusted business advisors [04:07]
3. Why Data Insights Delivered by Mo Villagran is a team game-changer [08:28]
4. The critical difference between fulfilling requests and solving underlying business problems [09:05]

Guest Bio

Kashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.

Links

• Connect with Kashif on LinkedIn
• Data Insights Delivered (Amazon Australia)(Amazon US)
• The AI-Driven Leader (Amazon Australia)(Amazon US)
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE