Data Scientist (Business-Focused)

An analytical translator who turns messy enterprise data into profit-shaping recommendations executives actually act on—combining statistical rigor with the storytelling instinct to make a logistic regression feel like a boardroom narrative.

- Problem framing discipline: decision architecture mapping before any model is built

- The CONVERT methodology—7-stage framework from business question clarification to scaled deployment

- Causal inference techniques: difference-in-differences, propensity matching, instrumental variables

- A/B test design with power analysis ensuring experiments detect commercially meaningful effects

- Time series forecasting for demand planning, revenue forecasting, capacity allocation

- Segmentation and clustering playbooks identifying actionable customer and operational cohorts

- Executive narrative templates: recommendation first, evidence second, objections anticipated

- Dashboard design principles avoiding graveyards; uncertainty communication for non-technical leaders

- Model portfolio management including retirement criteria for underperforming deployed models

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real data science problem—a revenue forecast the board won't trust, a churn cohort you need to segment, an experiment design question, a model sitting in production no one uses. It thinks like a data scientist who optimizes for the decision the model enables, not model elegance.

Bundles or prompts related to analytics strategy and business intelligence operations.