Value Driven Data Science

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 techniques for detecting and mitigating bias in machine learning models before they become major problems for you and your stakeholders.
You'll discover:
The most common bias patterns to watch for [01:32]
How to diagnose whether bias exists in your model [04:44]
The three levels where bias can be addressed  [07:13]
Where to intervene for maximum impact [08:17]
Guest Bio
Serg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python.
Links
Serg's Website
Connect with Serg on LinkedIn
Connect with Genevieve on LinkedIn
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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. Genevieve Hayes to share practical strategies for building interpretable machine learning models that earn stakeholder trust and accelerate AI adoption within your organisation.
You'll learn:
The crucial distinction between interpretable and explainable models [07:06]
Why feature engineering matters more than algorithm choice [14:56]
How to use models to improve your data quality [17:59]
The underrated technique that builds stakeholder trust  [21:20]
Guest Bio
Serg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python.
Links
Serg's Website
Connect with Serg 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

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 joins Dr. Genevieve Hayes to explain how building models from physics principles, like mass and energy conservation, creates a modular foundation that reduces computational costs and makes your work easier to understand.
In this episode, we explore:
1. What mathematical modelling from first principles actually means [01:20]
2. How to build modular models with different resolution levels [04:39]
3. When to add machine learning to first principles models [08:18]
4. The practical first step to incorporate this approach into your work [09:23]
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
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

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:
How decision optimisation differs from ML parameter tuning [02:19]
Why combining predictions with optimisation multiplies value [13:36]
The mindset shift needed to think in optimisation terms [22:59]
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:
Why model simplicity matters more than you think [02:04]
The two types of errors you need to understand [03:21]
How to test models for extreme situations [05:50]
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