Jyothismaria
Joseph

Some people got into data for the math. I got into it because I couldn't stand not knowing why things were the way they were.

I'm a data professional who genuinely loves turning messy, chaotic information into something clear, structured, and actually useful. Whether I'm profiling HPC jobs, building star-schema warehouses, trying to convince Tableau not to break my filters again, or wiring up AI agents to do the boring parts automatically — I'm happiest when I'm solving problems that actually matter to someone.

My work lives at the intersection of engineering and storytelling. I build pipelines, but I care deeply about what comes out the other end — whether that's a dashboard a non-technical stakeholder can actually use, a model that holds up under scrutiny, or an automated workflow that quietly saves someone three hours every morning. I've improved model accuracy from 14% to 62%, cut algorithm runtimes from 40 minutes to 5 seconds, and built AI agent systems that track competitor behavior in real time. That last runtime fix still brings me joy.

At my core, I'm a curious, detail-obsessed data person who gets genuinely excited about learning new tools, untangling complex systems, and using data to answer questions that matter — whether in supply chain, AI, research, or anywhere data decides to get interesting. If it's complex, ambiguous, and involves data — I'm interested.