Somewhere between a p-value
and a production deploy.

Forecast weekly parcel volumes across 37 customer-product lines. Benchmarked 14 models per line — 518 runs total — under rolling cross-validation. Per-line selection cut average forecast error by ~14%.

K-Means clustering on a 5,722 × 23 weekly-trajectory matrix. Found an 11% "peak-and-fade" cohort worth 6.5× more per customer than the segment absorbing most of the retention budget.

End-to-end pipeline cleaning 15 years of Destatis enterprise records. Established a 28.69% profitability benchmark and isolated productivity gaps — large firms achieving 2× the output of micro-operations.

Open-source Omdena challenge with a distributed international team. Built an ML pipeline for disease-outbreak forecasting and healthcare-facility accessibility mapping using historical and geospatial data.