Value Driven Data Science

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 episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how combining machine learning with decision optimisation creates solutions that go far beyond prediction, helping stakeholders make better decisions in uncertain environments.

You'll discover:

1. How decision optimisation differs from ML parameter tuning [02:19]
2. Why combining predictions with optimisation multiplies value [13:36]
3. The mindset shift needed to think in optimisation terms [22:59]
4. How to spot immediate optimisation opportunities in your work [23:42]

Guest Bio

Dr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course.

Links

• Get Tim's 3 Step Guide to Add Optimisation to Your Data Science Skills
• Bluebird Optimization Website
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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 lessons from deploying the Duckworth-Lewis-Stern method in high-pressure, real-time environments where mistakes have global consequences.

You'll learn:

1. Why model simplicity matters more than you think [02:04]
2. The two types of errors you need to understand [03:21]
3. How to test models for extreme situations [05:50]
4. The balance between confidence and humility [07:37]

Guest Bio

Prof. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.

Links

• Contact Steve at Bond University
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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 cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia.

This episode reveals:

1. How a single email response changed everything [05:24]
2. Why principles build trust where mathematics can't [13:19]
3. The "error whack-a-mole" problem that destroys credibility [16:00]
4. The real secret to creating work with impact [30:29]

Guest Bio

Prof. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.

Links

• Contact Steve at Bond University
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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

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 position to working as a data scientist in industry, revealing the challenges she faced and the strategies that helped her navigate this difficult transition.

You'll discover:

1. Why academic training can leave you unprepared for industry expectations [10:49]
2. The mindset shifts required when moving from research to business [07:50]
3. Strategies to help bridge the gap between academic and business work [15:23]
4. The one thing academics should do before leaving for industry [22:11]

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