In 2026, top companies don't just test algorithms. They test whether you can design production ML systems, reason about experiments at scale, write SQL that performs on billions of rows, work fluently with LLMs and agentic architectures, and communicate findings to people who don't share your technical background.

This playbook was built around that reality - not the interview landscape of three years ago.

• 300+ real questions from FAANG, top startups, and Wall Street - with full answer frameworks


• Resume and portfolio strategy for the age of AI screening


• Statistics, probability, and A/B testing at the depth interviewers actually test


• Machine learning and system design - algorithms, MLOps, production architecture, and model evaluation


• Advanced SQL, Python, and data engineering - window functions, pipelines, feature stores, and cloud platforms


• Generative AI and LLMs - RAG systems, agentic architectures, and LLM evaluation


• Product thinking, metrics design, and case studies - the rounds most technical candidates fail


• Senior and staff-level strategy - technical leadership, organizational influence, and salary negotiation

Whether you are breaking into data science, targeting a step up at a better company, or preparing for a FAANG loop - this book gives you the frameworks, mental models, and answer structures to handle any question the interview throws at you.

"The only prep book I found that actually reflects how hiring has changed. The system design and LLM chapters alone are worth the price."

"What separates this from every other book is that it teaches you how to think, not just what to say."