J'ai postulé en ligne. Le processus a pris 3 semaines. J'ai passé un entretien chez Google en sept. 2017
Entretien
I applied for it online in fall of 2017. A couple of weeks later a Google recruiter contacted me. He said to move forward I needed to first answer some essay questions, which included things like: describe an interesting project, how big was the sample size and number of variables, what challenges were there, and how did you solve it, etc. Then the recruiter set me up to talk with a member from the hiring team. The interview was conducted on a computer with video. He had a math PhD and was very nice . He first asked me some probability questions from sampling and then some coding questions, where I could choose the language. I chose R. The whole interview lasted about an hour. After about a week they told me that they would move forward with other candidates. Although I did not get an on-site interview opportunity, it didn't feel too bad since I wanted to do more machine learning but their group wasn't doing that.
Questions d'entretien [1]
Question 1
He first asked me how to come up with a reasonable metric to compare two methods (of doing something). He gave some hints for coming up with a reasonable metric. Then he asked me how to use this metric to determine which of the two methods is better, especially how to use a nonparametric way to do it.
- 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.
J'ai postulé via la recommandation d'un employé. Le processus a pris 2 mois. J'ai passé un entretien chez Google (Seattle, WA) en août 2021
Entretien
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
Questions d'entretien [1]
Question 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.
