Data Science

For founding-stage product DS and analytics-leadership roles at AI companies. 17 chapters on SQL, experimentation (A/B, MABs, causal inference), product metrics, predictive modeling for business, defining the data/experimentation stack, dashboards, and analytics leadership.

For senior/staff DS roles that ship production models end-to-end at fraud / identity-verification platforms and multimodal-sensor AI companies. 17 chapters on ML fundamentals, feature engineering, imbalanced data, prompt engineering, time-series & signals, data pipelines, production ML, MLOps, and AWS data stack.

Two sections in one guide. Section A: how DS / ML practitioners pick providers, run training and fine-tuning, manage experiments, and engineer cost on neocloud infrastructure. Section B: how DS teams *inside* neoclouds build pricing models, churn analysis, capacity forecasting, marketplace ranking, and anomaly detection on platform metrics.

Company-agnostic interview prep for the first-data-hire, Business Analytics DS role at a privacy-first AI company — the kind that runs on freemium subscriptions, a usage-based API, and a staking-for-compute token, and deliberately doesn't log user data. 11 chapters: analytics without surveillance, building the data stack from scratch, freemium + API + token metrics, privacy-safe experimentation, on-chain analytics, SQL, stakeholder communication, and the 30-60-90.

A standalone research dossier on Venice.ai, the privacy-first, uncensored AI company (Erik Voorhees, VVV / DIEM token economy, $1B Series A). How the product and its no-logging privacy architecture work, where the money comes from (subscriptions + API + staking-for-compute), and an honest market evaluation — competitors, the moat question, what works and what doesn't, bull vs bear.