J'ai postulé en ligne. Le processus a pris 2 semaines. J'ai passé un entretien chez Google (Mountain View, CA) en juin 2009
Entretien
Submitted resume online. Sailed through a 1-hour technical phone interview. The question involved bayesian analysis of drug testing (determining false positive rate, etc). It's a classic and it hit my sweet spot. Easy.
Questions d'entretien [2]
Question 1
The on-site interviews were not so easy. One thing to note: I have 20 years of experience and a strong track record of getting results. The interviewers had zero interest in what I've done before. Instead it was white boarding all the way. Felt more like an old-school math class -- go to the board and solve this problem -- than a job interview. Toughest question? Honestly, this was 4 years ago and I don't recall precisely. I think it was how to determine mean standard deviation of two blended datasets for which you know their means and standard deviations (and maybe Ns) but for which you've lost the original data. Turns out it's a classic you'll probably encounter in first year statistics, but I hadn't thought about it in about 25 years and so stumbled.
- 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.
