See how the model I built for Cotality could score your pipeline.
Adjust the deal parameters on the left. Win probability, risk tier, and the top drivers update live. Everything runs in your browser — no data leaves this page.
Deal inputs
Prediction
Top drivers
Recommendation
Not a toy. A miniature of a shipped tool.
The scoring logic in this demo is a lightweight logistic model with coefficients directly inspired by the four findings I isolated during my Cotality capstone: multi-product attach (H1), talk-ratio effect on win rate (H2), engagement cadence (H3), and Solution Engineer involvement (H4).
The real Cotality build added ML explainability (SHAP), a bootstrap-ensemble confidence interval, and a live upload path so leaders could re-score their pipeline the moment fresh CRM data landed.
What a hiring team gets, in ~3 weeks:
- A predictive model trained on your deal history
- An executive dashboard with per-deal drivers
- A live "what if" simulator like this one, built into the tool
- An in-app AI chatbot answering stakeholder questions from the data
- Documentation, tests, and a hand-off any analyst can maintain
I do the analytics and the interface. That’s the whole point.
Bring this to your team.
I'm interviewing for full-time Data Science, Analytics, and Product Analytics roles starting August 2026. If your team is thinking about pipeline analytics, sales-engineering coaching, or any “we have the data, we just can't see the pattern” problem — this is what I do.