There's a trick to data science interviews: Across sectors, they are surprisingly similar. Companies like Amazon, Google, and Meta have shaped the process, so you’ll encounter common themes almost anywhere.

Best way to improve fast: Track every question you’re asked. Later, in a low-stress setting, revisit and re-answer them. Similar questions are likely to appear again, and this habit can dramatically boost your preparation.

When I first transitioned from university and government roles to tech, I struggled with these interviews, but not anymore. A course I took helped me understand the motivations behind common behavioral questions and the expectations and rubrics underlying technical rounds. 

After taking it, I landed 9 offers in a row—9 for my last 9 final rounds including FAANG—by unlocking my classroom knowledge and applying it to tech industry questions and expectations.

This course:

• Helped me understand the motivations behind each interviewer’s role (e.g., hiring managers, senior ICs, business partners etc.).
• Clarified the intention behind common questions so I could provide targeted, thoughtful answers
• Provided hundreds of examples and structured practice opportunities to refine my skills.

Essentially, it unlocked the knowledge I had about experimentation, causal inference, and data analysis and allowed me to target that knowledge to solving common business problems in tech that I hadn't yet been exposed.

Details here (I did the lower-cost one labeled 'interview materials'): tinyurl.com/datascienceinterview 

Feel free to email me for details! 

(Affiliate note: I voluntarily reached out to this course provider to partner on a referral bonus, but had been recommending it for years.)

#datascience #jobsearch #interviewtips #datascienceinterview