Who am I?
I’m Jan. In my past life as a Neuroscientist I studied how the brain learns. This peaked my interest into complex dynamical systems, what laws govern them, and how they can be improved.
After transitioning into the Data Science and Machine Learning (ML) industry, I become fascinated by the complex dynamics that play out when data, software, and humans come together to build products.
All machine learning challenges are technical challenges. All technical challenges are people challenges.
It quickly became apparent to me that the machine learning algorithms, which could provide quick value to most problems, were well understood, but the technology to develop and scale out these solutions was not. In the 2010s many organizations were still wrestling with delivering web applications at scale, let alone ML engines. Overcoming these challenges required different processes and forms of organizing. Bringing ML products to industries that have existed without them is ultimately an exercise in change management.
In the past years I have helped organizations across industries to create and scale high-performance ML & Data Science teams and develop transformative AI strategies. With a strong technical background I’ve also helped teams design and deliver custom user-centered ML solutions using rapid development practices in the cloud.
I’m currently heading the Promo Optimization team at Loblaw Companies Limited, Canada’s largest retailer with over $52 billion CAD in revenue. My team builds machine learning-powered price optimization applications using modern cloud architecture and MLOps principles. In addition, I help drive Loblaw’s talent, technology, and engineering strategy to facilitate AI adoption.
What is this Site?
Over the years I’ve had the pleasure of meeting many extraordinary individuals that have shared their insights with me. This site is an attempt to capture these conversations and share them with a broader audience.
In particular, it’s an attempt to reflect on the state of the data, analytics, and ML industry holistically. What are the broad challenges that go beyond the technical implementation and the choice of algorithm? What are the factors that allow organizations to innovate and deliver value rapidly? What are the behavioural biases and organizational pathologies that stand in the way of success?
Of course, I don’t presume that I have the answers to all of these questions, nor will my writing ever get close to some of the great thinkers in the industry. So please enjoy and don’t fret. I wouldn’t be the first to be wrong on the internet.