Data Scientist applicants have rated the interview process at X with 3.2 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 31% positive. To compare, the company-average is 39.4% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Scientist roles take an average of 29 days to get hired, when considering 35 user submitted interviews for this role. To compare, the hiring process at X overall takes an average of 29 days.
Common stages of the interview process at X as a Data Scientist according to 35 Glassdoor interviews include:
Phone interview: 51%
Presentation: 19%
One on one interview: 16%
Group panel interview: 7%
Background check: 5%
Skills test: 2%
Here are the most commonly searched roles for interview reports -
I applied through an employee referral. The process took 3 weeks. I interviewed at X (San Francisco, CA) in Sep 2015
Interview
phone interview with hiring manager, got onsite invitation the same day
onsite, meet with 6 engineers. one's data scientist, all others are software engineer.
algorithm question mostly, some machine learning question, math/application case.
also, one system design question. how large scale machine learning work on distributed system
positive feedback, went through hiring committee. However, no offer yet. They're cutting jobs, so bad timing!
Interview questions [1]
Question 1
algorithm(50%), machine learning(30%), system design(20%)
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.