Data Scientist interview case study: Stripe

Last updated: Dec. 20, 2023
2 mins read
Leon Wei
Leon

 

As of July 2021, Stripe is still a Pre-IPO company which means if you join them soon, there is still a vast potential for you to become a millionaire after their IPO.

Stripe has many data science roles that are currently open. Each team hires for their own; therefore, different groups may have slightly different cultures and expectations from a candidate. 

But generally speaking, the interviewing process is not significantly different than other tech companies.

Depending on your work experience and the level you are applying for, you will likely be assigned a take-home assignment that can take a day or so for you to analyze the data, build some models, and finish a write-up report.

I got a chance to help Jerry prepare his job search, he recently went through a Stripe interviewing process and really enjoyed it. 

Even though he decided to join another pre-IPO tech unicorn company for personal reasons, he kept telling us how great the overall interviewing process is with Stripe.

The recruiters are very professional who genuinely want him to succeed, friendly interviewers who focus on critical technical questions, and very sharp potential future teammates.


Here is the overall timeline:

Week 1: hiring manager phone screen

The hiring manager shared basic info about their team, problems they are focusing on, expectations about this role. A few questions on Jerry's resume, and asked a business analytics question.

 

Week 2: take-home assignment

It comes with a sample data set, was given a week to complete the analysis and report. 

The assignment was not super hard but requires a combination of basic unsupervised learning and supervised learning to model.

The data does need some cleaning up and pre-processing.

 

Week 3: passed take-home assignment and scheduled final round interview.

The recruiter contacted and asked to pick a date for the final round of interviews.

 

 

Week 5: Final interview.

Six rounds overall.

Round 1: Data Processing with SQL;

Round 2: Statistics: forecasting model, how to evaluate the model performance;

Round 3: Presentation (based on the take-home assignment). Explaining findings to non-technical (business partners) in plain language.

Round 4: Product sense. A/B testing, how to decide sample size, how to deal with a situation when the test results are not significant

Round 5/6: Leadership/Behavioral questions: why Stripe? How do you deal with conflict?

 

Acknowledgment: we want to thank Jerry to share his wonderful interviewing experience at Stripe, and wish him good luck on his new gig.

If you ever need some help with data scientist interview preparation, please feel free to hit us up.



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