Preguntas de entrevista para Senior Data Analyst de Evernote

Actualizado el 18 may 2017

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Candidato de entrevista anónimo en Redwood City, CA

Sin oferta
Experiencia positiva
Entrevista promedio

Acudí a una entrevista en Evernote (Redwood City, CA) en abr 2017


Fairly standard process. 1) Recruiter phone screen. 2) Analyst on the team phone chat. There was some shared coderpad SQL and higher level questions on running tests. Very attentive, patient, and pleasant talk. 3) In person at Redwood City. Met with about 6 people in 4 hours - the 2 other analysts, CFO, product manager, growth manager, and a data engineer. Not as technical as other Senior Data Analyst roles. Questions were really strategy and communication focused rather than model evaluation, adjusting for bias, etc... There was a case study (which I never had before) in Excel (which I haven't touched in ages), some warmup probability, and whiteboard sql. Everything else was not really as defined. More of "How would you attempt this vague scenario, and how would you use or communicate the results afterward". I come from more of the data engineering and modeling end of spectrum of data analysts and was not too surprised with the rejection feedback. If you have management, consulting, or lots of experience working with internal business partners (product management, marketing, etc...) yet also have a strong analytics toolbox, you should do great. I specifically want to call out the recruiter, Melanie. She was remarkably open and candid through the whole process. After the rejection email, she asked to jump on the phone to further discuss the decision process and help me further with my search. This is so rare in the bay area tech world, and especially in data analysis roles that tend to have MANY qualified candidates. The whole interview experience was very warm and encouraging at Evernote and if you happen to be hired, I'm sure the company will be just as great.

Preguntas de entrevistas
  • High level strategic and intentionally vague case study type questions. You can probably imagine the questions about defining product KPI's, measuring success, testing, and communicating actionable steps. They are much less interested in mathematical, statistical, or coding chops.
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