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:

1. The most valuable skills academics bring to industry [01:30]
2. Why the experimental mindset matters so much [03:43]
3. The hidden benefit of extended research projects [04:54]
4. 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 LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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 explore how unexpected hobbies and activities can make you a more effective data scientist and enhance your career.

You'll discover:

1. How dancing skills translate into better stakeholder presentations [02:02]
2. What swimming teaches about working with resistance [06:30]
3. Why coaching swimmers improves communication with non-technical colleagues [08:10]
4. The simple activity anyone can try to expand their data science thinking [11:03]

Guest Bio

Colin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.

Links

• Connect with Colin 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 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 applications that go far beyond code generation, including techniques for processing unstructured data, improving stakeholder communication, and identifying blind spots in your analysis.

You'll learn:

1. How to use LLMs to extract structured insights from messy unstructured data [02:50]
2. The role-playing technique that helps you practice difficult stakeholder conversations [14:12]
3. Why using multiple LLMs helps reduce AI hallucinations [20:38]
4. A step-by-step approach for integrating LLMs into your workflow safely [25:52]

Guest Bio

Colin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.

Links

• Connect with Colin 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

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. Genevieve Hayes to share practical LinkedIn strategies that can transform your data science career.

In this episode, you'll discover:

1. How Sarah went from posting twice a year to daily LinkedIn content [01:25]
2. The biggest benefits of consistent LinkedIn posting for data science careers [03:15]
3. How to manage the challenge of daily content creation without burnout [04:31]
4. The one LinkedIn strategy every data scientist should start using tomorrow [08:47]

Guest Bio

Sarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.

Links

• Connect with Sarah on LinkedIn
• Dub Dub Data Website
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

There was once a time, when data science was still in its infancy, when demonstrating any attempt to learn Python or machine learning was enough to secure a job interview. The demand for data scientists massively outweighed supply. 

Ten years later, however, the job market has dramatically shifted - and many data scientists who unexpectedly find themselves out of work face a truly overwhelming experience.

In this episode, Sarah Burnett joins Dr. Genevieve Hayes to share how she transformed redundancy from a senior banking role into the launch of her own successful data consultancy, proving that unexpected job loss doesn't have to mean career disaster.

In this episode, we explore:

1. Why redundancy is a numbers game, not personal failure [03:54]
2. The power of taking time to process after job loss, instead of rushing back [08:47]
3. How to pivot when your first business idea doesn't work [16:58]
4. Why building side projects and community involvement create career insurance [20:52]

Guest Bio

Sarah Burnett is the co-founder of Dub Dub Data, a consultancy that offers human-centric AI and Tableau solutions. She transitioned into independent consulting after navigating redundancy from a senior role at a major bank. She is also the co-host of the podcast unDubbed.

Links

• Connect with Sarah on LinkedIn
• Dub Dub Data Website
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

When most data scientists think about their competitive edge, they focus solely on what goes on their resume - education, work experience, and technical skills. But what if the things that truly make you irreplaceable go far deeper than your LinkedIn profile? 

Your family background, cultural influences, communication quirks, and even the hobbies that make you nerd out all contribute to what makes you uniquely valuable.

In this Value Boost episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore the concept of your "untouchable advantage" - the unique combination of experiences and qualities that make you impossible to replace as a data scientist.

You'll discover:

1. Why your untouchable advantage extends far beyond your technical qualifications [02:09]
2. How family influences and personal quirks become professional superpowers [04:14]
3. Why introverts have unique advantages they often don't recognize [10:36]
4. The simple way to uncover your own untouchable advantage starting tomorrow [14:08]

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

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