Envié una solicitud electrónica. El proceso duró 6 semanas. Acudí a una entrevista en GEICO (Bethesda, MD) en ago 2019
Entrevista
Brief phone screen, take-home prediction challenge, 4-hour on-site. I felt that the interview went extremely well, and was frankly surprised to not get an offer. I suspect that the reasons were fairly idiosyncratic to GEICO, given that I had good answers to everything technical that I was asked, and don't *think* that I made any gaffes. Fairly annoyed by the amount of time that the interview took -- code challenge, 4-hour onsite -- given this outcome.
Preguntas de entrevista [1]
Pregunta 1
One hour lunch, one hour presentation of technical challenge (grilling on technical detail), one hour with manager (trying to assess culture-fit), one hour with tech lead (mostly describing projects that I've worked on).
Solicité el puesto a través de un captador. Acudí a una entrevista en GEICO en oct 2022
Entrevista
HR was very patient. The whole process was smooth and fast. 4 rounds of interviews in total. The first round was ML+resume; the second was python + SQL + easy algorithm, the third round was real case machine learning models, the last round was with one of the managers and asked resume and one machine learning model.
Preguntas de entrevista [1]
Pregunta 1
Asked many machine learning questions such as decision tree, random forest, KNN, and Kmeans. Must know the detail of each model very well. Deep understanding of your resume, asked many details
El proceso duró 3 semanas. Acudí a una entrevista en GEICO
Entrevista
Onsite technical screen had SQL and python coding questions. Format was in a double spaced google doc, which is by far the worst possible way to ask these questions. Interviewer didnt seem to understand the problems during the interview leading to confusion. GEICO, like many financial or insurance companies, have really poor interview processes in place for data science. Bottom quartile compensation = bottom quartile talent.
It was a very strenuous and long process which required a lot of time spending through multiple rounds of interviews and take home exam. Many technical questions regarding ML from multiple interviewers.