Ho presentato la mia candidatura online. La procedura ha richiesto 5 settimane. Ho sostenuto un colloquio presso Google nel mese di lug 2019
Colloquio
Applied online in late May as a recent PhD graduate. E-mailed by a recruiter within a week, who scheduled a phone call with her for mid-June and had me fill out a questionnaire describing my data analysis experiences and statistical skills. The phone call was 5 minutes where she told me the next step would be a technical Hangouts interview with a Data Scientist and to study applied statistics and programming in a Google Doc. My interview was scheduled for early July. In the Hangouts interview, he asked just stats questions, no programming. I was unable to solve the last problem, he didn't really provide any hints. The next day the recruiter told me I would not be moving on.
Domande di colloquio [4]
Domanda 1
Describe Type 1 and Type 2 errors without introducing more lingo.
1/1000 people have a disease, there's a test that is 99% correct if you have the disease. If you don't have the disease, 2% error rate. If someone tests positive, what are the odds they have the disease?
- 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.
