Over the last 10 years, I’ve worked in various data and ML roles at Amazon and AWS. One of the perks of working there? Never a shortage of data—so I got used to working with terabytes, always digging for insights. Since many projects made it all the way to production, I had to build solid data engineering skills early on, and they became second nature.
On the data science and ML side, we developed all kinds of models, but one theme kept surfacing: businesses are always interested in the “why”—those causal questions. I’ve written about causal inference models like DoubleML, synthetic control, and Difference-in-Differences, which we’ve used in many projects to get to the heart of those questions.
My biggest takeaway from my time at Amazon? Learning how to scope, plan, and manage data science projects end-to-end—a topic I dive into more in this post.
Before Amazon, I spent years in software development, which comes in handy for building complete data products from scratch.
If you’re looking for help with a data project or want to chat about how machine learning could fit into your business, feel free to reach out!