MADinterview is the new one-stop platform for your machine learning and data science interview prep.
Go premium to access 103 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 |
|---|---|---|---|
| Statistics | 20 | 5 | 1 |
| Machine Learning | 17 | 4 | 1 |
| Probability theory | 16 | 4 | 1 |
| SQL | 15 | 6 | 2 |
| A/B Testing | 13 | 2 | 1 |
| Application process | 8 | 8 | 0 |
| Machine Learning Coding | 5 | 3 | 1 |
| Metrics | 4 | 2 | 0 |
| Data Structures and Algorithms | 2 | 2 | 2 |
| Technical deep dive | 2 | 2 | 0 |
| Behavioral | 1 | 1 | 0 |
| Total | 103 | 39 | 9 |
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.
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.
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! :)