MAD interview is currently in Beta. Please send all your comments and suggestions at comments@madinterview.com. The platform is updated weekly and expected to be fully fuctional sometime in H1 2025.



MADinterview is a new platform that prepares candidates for their machine learning and data science interviews through:

  • 17 machine learning and data science interview questions and their solutions. (when completed, the platform will offer 500+ questions with answers)
  • Learning material covering 47 machine learning and data science interview topics. (when completed, the platform will cover 100+ relevant topics)

Why Choose MAD for Your Interview Prep?


MAD is dedicated to becoming the premier platform for Machine Learning and Data Science interview preparation: it offers a holistic learning framework that focuses on targeted topics, covering the entire Machine Learning and Data Science interview loop comprehensively.

About Me

I am currently a Staff Applied Scientist at a unicorn startup and have previously held roles as a Staff (IC6) Research Data Scientist and later Product Data Scientist at Meta. I hold a PhD in applied ML and an undergraduate degree in Engineering. Before Meta, I spent seven years as a researcher and academic, teaching data science at a top-tier liberal arts college in the northeast US. My diverse background and academic experience allowed me to interview for and receive offers for MLE, Applied Scientist, Data Scientist, and Product Data Scientist roles from top-tier companies such as Google, LinkedIn, Amazon, and Snap.

About MAD

I created MAD as a side project out of my own frustration during transitioning from academia to industry and while preparing for interviews. A typical ML (DS) interview loop includes:

Despite this structure, most online resources focus primarily on coding (Data Structures and Algorithms and SQL), which only accounts for about 20% of the interview process. Some interview preparation platforms do offer incomplete preparation in probability, statistics, A/B testing, and ML concepts. However even these platforms often do not provide solutions or use solutions from user-generated content, which can be inaccurate and misleading.

Additionally, there is no concentrated, role-specific learning material for candidates. Typically, candidates have to refer back to their college notes, take multiple courses on Coursera, download slides from CS229, and randomly skim through probability/stats and A/B testing books. MAD aims to consolidate all this into role-specific online books, covering the fundamental knowledge candidates need to brush up on before their interviews.

Below is an outline of the areas MAD aspires to cover, making it the go-to platform for Machine Learning and Data Science interview preparation. If you have any comments or suggestions, please email me at comments@madinterview.com.

Topic Data Structures and Algorithms (DSA) Interview-prep Platforms Data Science Interview-prep Platforms MAD (when completed, expected in H1 2025)
Coding, Data Structures and Algorithms Best coverage Some coverage Curated list of the type of DSA questions that can be asked in ML/DS interviews (50+ questions and answers)
Coding, SQL Some coverage Best coverage Curated list of the SQL questions that cover all concepts asked inr Product Data Science and Data Science interviews (50+ questions and answers)
Coding, Machine Learning Concepts Do not cover Do not cover Extensive coverage (10+ frequently-seen-in-interview ML-concept coding questions and answers)
Coding, Machine Learning on Real Data Do not cover Coverage of take-home case studies Coverage of freqently-seen hour-long interview sessions for applied science/MLE roles (5+ solved ML modeling problems from real interviews)
Probability and Statistics Do not cover Good coverage, but often without answers Extensive coverage, broken down by topics, with proper solutions and references (100+ questions and answers)
A/B Testing Do not cover Some coverage, but often without answers Extensive coverage (50+ questions and answers)
Machine Learning Do not cover Some coverage, but often without answers Extensive coverage (200+ questions and answers)
Machine Learning System Design Do not cover Some coverage, but often without answers Framework for preparing on how to answer these questions plus system design knowledge questions (20+ questions and answers, 5+ system design problems)
Metrics and Root Cause Analysis Do not cover Some coverage, but often without answers Framework for preparing on how to answer these questions plus metrics-specific knowledge questions (10+ questions and answers, 5+ root cause analysis problems)
Behavioral and Prior Experience Do not cover Do not cover Framework for preparing on how to answer these questions (20+ questions to prepare)
Focus on Learning Limited Limited Extensive coverage of the fundamental knowledge that a candidate needs to have before interviewing (100+ relevant topics)
Role-specific Interview Prep Do not cover Do not cover Interview-prep material curated for Product Data Science, Data Science, Applied Science, and Machine Learning Engineer roles

Enjoy prepping! :)