Hi there — welcome to the site.
I figured the right way to kick things off is with a proper introduction. So, hey, I'm Kirby. I'm a data scientist - in all that means in 2026, and this is my little corner of the internet where I plan to write about things I find interesting, share things I'm building, and generally think out loud in public.
Who I Am
Right now, I work at a customer experience analytics company where I work in the DS department. I have been at the company almost 9 years now and have held multiple different positions within different departments.
Throughout my time in DS I have been able to work on many things including:
- Financial + CX linkage ML models
- Training and putting into production encoder-based (BERT) and fine-tuning decoder-based (Llama, Mistral) models for predictive metadata tagging in data ingestion
- Production conversational multi-agent systems as "data analysis assistants"
My path into AI wasn't a straight line. I went to school for marketing research and found my first role in traditional research approaches, survey theory, and statistical data analysis.
Through my time as a researcher (in the traditional data scientist/data analyst sense) I got many great opportunities to hone my skills of "finding the insights" in data, creating a result narrative that made sense and drove to clear actions, and present those results to many internal and external, including VP and C-Suite level, client stakeholders.
However, I was always amazed at how cool the data scientists were at the company, so when I saw a chance to join them I leveraged my way onto the team and haven't looked back! I went from someone who analyzed customer feedback to someone who builds systems that can reason about it autonomously.
What I'm Into
At work, I live at the intersection of AI engineering and data infrastructure in my day to day. I have tried to assemble skills A-Z across what a data scientist could touch. I truly love creating a model or a product that will have true impact on the company or make someone's life much easier. The umbrella term of data science can mean so many things, and I love that you get to learn something new under it every single day!
After hours, I have a recent addiction to F1 and all things racing (even got a beginning of a race sim!). I love sports and I love hanging with mates talking and watching sports. Outside, I am usually playing with my dog Harry - Sir Harold is his gov't name - or golfing.
On the learning side, I try to learn as much as I can after work - whether that is a new blog to read, a new paper that I see on X, or listening to a recent episode of Latent Space or something similar. Bridging the gap between academics or other industry examples back to my day to day job is always a fun challenge I embrace. Ask me what I have been up to lately - I keep a running tracker of everything I've read — partly to stay sharp, partly because the field moves so fast that last month's breakthrough is already old news.
Why I Built This
I wanted a place that was mine. Not a LinkedIn profile, not a GitHub README, not a tweet thread. Just a space where I can write at whatever length I want, about whatever I want, without an algorithm deciding whether it deserves to be seen.
This site is minimal by design. I don't want it to be a portfolio full of flashy animations — I want it to be a place worth reading.
What To Expect
I have been reading about RLMs (recursive language models). I want to write up a blog on that trying to explain my understanding of them... they are amazing IMO!
I also have a couple of projects that I am getting farther and farther behind on - hoping writing about them will keep me on track. One being a agentic system for seeing new articles, blogs, and papers from my database of AI sources - summarizing them and storing them in my Notion database. Another project being a deep dive into my music taste - can I create a service that will give me a answer to "will I like this" before I even listen?
I'll write when I have something worth saying. No posting schedule, no commitment to a niche. If that sounds good to you, stick around.
Thanks for reading all the way to the end. I'll see you in the next one.