Ho presentato la mia candidatura tramite segnalazione di un dipendente. La procedura ha richiesto 3 mesi. Ho sostenuto un colloquio presso Google (Mountain View, CA) nel mese di mag 2012
Colloquio
After a referral from a current employee, it was a long process (3 months). Long enough, in fact, that the job listing itself changed several times over the course of the process. An initial call from a recruiter, two (1hr.) technical phone interviews covering some basic data analysis, technologies, and relevant items from the resume. Invited to fly out for a on-site in Mountain View, involving 4 interviews with analysts from different business segments. Took a few weeks to get an answer after that.
Domande di colloquio [1]
Domanda 1
There was one with 'what-if' questions involving how to analyze certain aspects of their business, including finding trends in ad bidding behavior and detecting contextual advertisers who were gaming the system. Open-ended enough that it wasn't clear if a right answer existed, but they may have had one in mind.
- Asked foundational questions about key definitions and terminology to assess baseline understanding of core concepts
- Completed a timed online coding assessment covering practical programming challenges and problem-solving ability
30 minute phone screen with HR, followed by an interview with the hiring manager. HR would not even provide a salary range for the role, which was very weird. The HR rep was not familiar with the role and seemed to be reading from the JD when I asked questions about it.
Ho presentato la mia candidatura tramite segnalazione di un dipendente. La procedura ha richiesto 2 mesi. Ho sostenuto un colloquio presso Google (Seattle, WA) nel mese di ago 2021
Colloquio
Recruiter screen > tech screen > 5 tech sessions at remote "onsite"
Tech screen: all statistics written in easy python
On-site: python for SQL-style queries, one session focused on stats/probability, majority of sessions had some probability in it, some question were extremely open ended, hierarchical statistical models, optimization and creating penalty functions, bootstrapping, small sample statistics
Domande di colloquio [1]
Domanda 1
you are given a discrete probability distribution of children, what is the probability a random women you meet on the street has a sister?
Two variables x1 and x2. They are correlated but aren't the same. X3 = X1-X2 and X4 = X1+X2. What are the coefficients for x1 and x2 if you train logit for x3 and x4
1000 ad videos, 1000 human raters
Assess the quality of videos, 100 randomly selected videos to each rater, Rate video between 1 (bad) and 10 (good) quality. How would you rate these? What are the pros and cons of your strategy?
clustered statistical modeling question about how you would set data up for this model and what model you would use.
