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 — like the production lead-scoring model I shipped that cut manual review labor by 80%. Open to full-time data science roles, available immediately.
| Production ML | 70 |
| Statistical rigor | 65 |
| Data visualization | 70 |
| Optimization | 60 |
| Baseball IQ | 80 |
| OFP | 70 · immediate call-up |
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)
The
evidence.
Seven projects with the highest signal: production deployments, original research, and advanced coursework with measurable results I'll defend in an interview. Every number below is real. The full catalog of 30 lives in the chart that follows.
Every project,
plotted.
Thirty projects spanning foundational coursework through production AI, plotted by year and domain. Scroll to read the arc, or take the wheel with the filters. Hover any point for the title, click to open the file.
Thirty projects, four seasons.
Every dot is a real, finished piece of work — coursework, research, and production systems. Read it like a development arc: low-A ball on the left, the majors on the right.
Foundations first.
Intro Python on real datasets — 50K Airbnb listings, 100K IMDb rows, NOAA storm records. Unglamorous, necessary, fast.
The deep end of the curriculum.
PyTorch CNNs, reinforcement learning, CRF sequence labeling, QCQP optimization in Julia, permutation tests built from scratch. Method over demo.
Production, with receipts.
Real company, real stakes: a deployed lead-scoring model that cut manual review labor 80%, and an inspection app that replaced a paid SaaS contract company-wide.
Live systems, running now.
A 3D pitch-tunneling tool covering all 596 qualified MLB pitchers, a production movie recommender, a quant trading bot sizing real money. Click any of them.