I applied online. The process took 1 week. I interviewed at X (San Francisco, CA) in Jun 2016
Interview
I was contacted fairly quickly after applying online. I didn't find any of the interview questions very hard. What struck me was the type of questions: how-do-you-solve/do-xyz, where xyz is specific. Nowadays it seems any job involves data analysis is called data scientist. As a statistician, my job search 95% of the time led me to data scientist position postings. In the pre-data-science period, statistician interviewers were composed of at least half internal data customers from various domains. They wanted to know how I can help them achieve their research or business goals with data. In contrast, all of my data scientist interviewers (not just at Twitter) were younger and more computing savvy data analysts from quantitative background. They wanted to know if I know how to do xyz, where xyz is something they themselves have encountered. This difference is important for candidate and employers of data scientist.
Interview questions [1]
Question 1
What features would you use to build recommendation algorithm for users.
The recruiter got in touch to set up a screening call. I was asked basic questions concerning my background and my motivation. Then we had a coding challenge with a question I later found on LeetCode under the Twitter section for the last 6 months.
Case study was interesting; interviewer was previously from uber so some similar interview questions, techniques do apply, overall a good engaging exercise. Nothing to complain about. Overall it is okay
Interview questions [1]
Question 1
explain probability distribution, how to track cohorts, a/b testing, case study on casual inference, working sample codes based on sample user behavioural usage dataset.
Python Coding of data science algorithm. Python library fundamental knowledge questions.
Data structure and algorithms coding.
System design of distributed compute systems.
A behavioral question round.
Followed by a hiring manager round.
