Conner
Toryfter.
Data scientist and applied mathematician, just out of UW–Madison. I'm drawn to the practical edge of this field — where careful math earns its keep against the numbers a business actually watches. Open to full-time data science roles, available immediately.
On the
record.
I'm past the notebook stage. The work I find most rewarding is the full loop — framing the problem with stakeholders, training and tuning the model, getting it into production, then watching the metric move. I've done it once already at an enterprise partner, cutting manual review labor by 80%. The next time is the one I'm looking for.
Academic depth backs that experience. UW–Madison's joint Data Science + Mathematics program built fluency across the stack — deep learning in PyTorch, reinforcement learning on Gymnasium, CRF sequence labeling, QCQP optimization in Julia, and the full statistical inference toolkit. I read papers in my downtime and publish original research on the side.
What I'm looking for: a full-time data science or applied ML role, starting as soon as the right fit lands. Strongest match on teams where I can own a problem end-to-end and contribute to real business outcomes in week one. Open across industries and locations. Quick to ramp, ready now.
"He gets on base." — Peter Brand, Moneyball (2011)
Featured
cases.
Six projects with the highest signal. The first two are shipping production AI at Kriete; the rest are independent research and advanced coursework with measurable results I'd defend in an interview.
Every project,
plotted.
The complete catalog — 26 projects from foundational coursework through shipped production AI — plotted by year and domain. Hover any point for the title, click to read the file. Filter chips narrow to a category.