Bob came to me in tears. He showed me his prep materials for his last data science interview with over 100+ coding and algorithms questions completed. But he failed his Meta technical screen when they asked him “What metrics would you track to improve Facebook Groups?”

We spent two hours going over the frameworks for the product analytics interview in preparation for his next LinkedIn one. And then I assigned him 5 case studies. Two weeks later he was staring at a $276K total compensation offer.

During my six-year journey as a data scientist, I found myself on both sides of the interview. I started my career stumbling over linked lists and product analytics questions. And the tail end of it was spent slowly shaking my head at bright eager candidates that couldn’t pass my company’s first technical interview question.

And then I decided to quit my job and Interview Query was born.

Bob was in that same group. It wasn’t that he lacked effort. His preparation was just off-base, he didn’t know what to study or focus on, overwhelmed by the sheer breadth of topics he felt he "should" know.

Data science and engineering interviews span 10 to 12 different topics. And our curated questions bank, detailed company guides and courses are designed to focus your studying on these critical areas, ensuring that you have a roadmap crafted from real experience towards your goal.

Interview Query is the essence of countless stories, experiences, and shared dreams from interviews. It was built by data scientists for data scientists.

And even when the theory was mastered, translating it into practical solutions in an interview setting was a challenge in its own right.

I want everyone to have the same kind of opportunity to land their dream job like Bob did.

Bob, like many, didn’t know that Meta’s data science role focused on SQL and analytics rather than intensive coding.

Thanks for checking out Interview Query, and best of luck with your interviews.