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MADinterview is a new platform for Machine Learning and Data Science interview preparation, created by a staff ML scientist at a leading unicorn startup (formerly at Meta).

Go premium to access 144 answered questions, role-specific curated learning material on 8 topics, "the bar" book collections, and more.

Or just try out as a guest a small free samle of questions and answers

Topic Premium questions Free (Gmail) questions Free (guest) questions
Machine Learning 48 15 11
Statistics 20 8 6
Probability theory 16 8 5
SQL 15 8 4
A/B Testing 13 4 4
Data Structures and Algorithms 9 9 9
Application process 8 8 4
Machine Learning Coding 8 3 2
Metrics 4 2 2
Technical deep dive 2 2 2
Behavioral 1 1 1
Total 144 68 50

Why MAD


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 Staff (IC6) Product Data Scientist at Meta. I have years of experience conducting data science interviews and creating relevant interview questions. 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 Good coverage Curated list of SQL questions that cover all concepts asked inr Product Data Science and Data Science interviews (50+ questions and answers) plus an SQL playground with a real database schema to explore
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! :)

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