Hi — I’m Greg Duckworth

I’m a Data Scientist with over a decade of experience and a PhD in Applied Statistics from the University of Cape Town, South Africa. I’ve built, scaled, and delivered data-driven solutions across startups and large enterprises, leading teams from small, high-impact groups to cross-functional enterprise teams.

My work spans machine learning, generative AI, predictive analytics, and computer vision, but the focus has always been the same: solving real-world business problems and delivering measurable impact.

After a decade in the ML & AI industry, I created this space to share my perspective and reflections. Not just what works, but how to think through challenges, explore new ideas, and navigate a rapidly-evolving landscape. My hope is that others might find these observations useful, and perhaps see new ways of approaching their own work. If so, please get in touch! I'm always interested in hearing from other implement data-driven solutions!

Topics I write about

Real-world ML Projects
Reflections from putting machine learning models into production: what tends to work, what often goes wrong (and why), and the pitfalls I’ve seen teams encounter. I write about my experiences with designing, deploying, and monitoring models in practice, and the lessons those projects taught me.

New AI & ML Methods
Being a statistician at heart, I'm naturally drawn to new methods to analyse old problems. I'm particularly interested in new methods that improve on the performance in tricky situations: think things like anomaly detection (fraud), recommendation systems (next best offer), time-to-event models (e.g., customer churn), or extreme value models (e.g., demand spikes). 

Trends & Emerging Tech
Observations on which AI and ML trends are genuinely transformative versus which are mostly hype. I reflect on how these trends play out in real projects and share the ideas that have made me rethink approaches or experiment differently.

AI Strategy & Leadership
Thoughts on navigating decisions, trade-offs, and prioritisation in AI & ML initiatives. From choosing which use cases to explore to aligning projects with business context, I share what I’ve learned from leading teams and running projects in the real world.

My Philosophy

I approach AI & ML like a scientist: forming hypotheses, testing assumptions, and interpreting outcomes objectively, even when data is messy or organisational processes are inconsistent. 

This lens guides everything I write, from project reflections, technical implementations, to critiques of emerging trends.