Topic

Data Science

Interview prep for Data Scientist roles at modern AI companies — grouped by the two archetypes hiring loops actually test for: product/analytics depth, and full-stack applied modeling.

Guides

3 guides

Product & Analytics 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.

Interview Prep SQL & Experimentation Causal Inference Product Metrics Full-Stack & Applied Data Science

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.

Interview Prep Production ML Fraud & Identity Time-series & Signals Data Science for Neoclouds

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.

Deep Dive GPU compute Neocloud ML & Analytics