Ich habe mich online beworben. Der Vorgang dauerte 1 Woche. Vorstellungsgespräch absolviert im Mai 2015 bei Databricks (San Francisco, CA)
Vorstellungsgespräch
solved 2 pre screening questions online when applying on their website. Then was reached by the header of data science and machine learning in data bricks. The header initially called and learned my preference, then followed by a tech phone interview asked similar question as the prescreening ones. The SQL questions include join, having,group by. The coding question's choice of language is up to you, python is preferred. But R is also fine. If you could know java or c, thats better.
Fragen im Vorstellungsgespräch [1]
Frage 1
SQL;coding questions. Questions are not too hard. But I think they want you to be able to solve without much thinking, with speed.
The interview questions includes from ML concepts to python design, which is harder than I expected. But can use SQL as well. You can choose what you want to use.
3 rounds, 1 case interview, 1 fundamentals and 1 SQL interview. The case interview was extremely technical and the fundamentals interview didn't cover my resume at all. Overall it was quite difficult, and the fundamentals interview prep sheet was not helpful at all
Fragen im Vorstellungsgespräch [1]
Frage 1
Q1: what are the assumptions of linear regression
Q2: would a random forest overestimate or underestimate revenue compared to a ARIMA model when forecasting average revenue
Ich habe mich auf Empfehlung eines Mitarbeiters beworben. Vorstellungsgespräch absolviert bei Databricks
Vorstellungsgespräch
Recruiter call, 30 min HM interview, 45 min technical call, take home assignment (GenAI focused project), then multi-hour virtual onsite covering ML engineering, ML technical fundamentals, statistics, and behavioral questions/career aspirations. Did not advance past the technical call
Fragen im Vorstellungsgespräch [1]
Frage 1
Explain the transformer architecture and how it is different than other autoregressive models