Ho presentato la mia candidatura online. La procedura ha richiesto un giorno. Ho sostenuto un colloquio presso Google (City of London, England) nel mese di mag 2011
Colloquio
First a discussion with the interviewer about my phD research, 35mins, he asked a lot of questions about the research top and the methodology; next, some questions related to basic econometrics, but requires deriving formula (e.g. to derive the variance of an OLS estimator from the OLS estimate itself); the next question is "how you evaluate the effectiveness of google mail with respect to persuading people using google more frequently than our competitors, say, yahoo, you can imagine you can get what ever data you could, - tell me what data you would like to collect and how you want to use them, and what potential insights would you expect to get out of them?"
Domande di colloquio [2]
Domanda 1
derive the formula for the variance of OLS from scratch
how you evaluate the effectiveness of google mail with respect to persuading people using google more frequently than our competitors, say, yahoo, you can imagine you can get what ever data you could, - tell me what data you would like to collect and how you want to use them, and what potential insights would you expect to get out of them?
- 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.
