J'ai postulé via la recommandation d'un employé. J'ai passé un entretien chez Amazon (Seattle, WA) en sept. 2020
Entretien
It was the phone interview. The interviewer was changed and he was not in the group that I was considered for. I believe he did not know I was being evaluated for an entry level position (L4) in Amazon. He asked only technical questions. He asked about algorithm, computing time and complexity of a python code. He asked the theory behind L1 and L2 regression, and many tough questions that I assume are both irrelevant to data science and too technically complicated to be understood in college data related courses.
I think they should be more keen to the people who interview to understand what position its being considered for and what should be a typical question to be asked for it.
Questions d'entretien [1]
Question 1
- Why we have L1 and L2 regression regularizations but no L0.5 or L4?
- Right a python code for recognizing if entries to a list have same characters or not. Then what is the computation complexity of it?
- Right a complicated SQL code, turned out later that it actually was only solvable with a postgreSQL code while it was not in the job description.
- What is over fitting.
- what to do with unbalanced data.
There are three rounds in total. The process begins with a coding round, followed by the main interview loop, where you will meet the team and discuss technical skills, experience, and fit.
First round is fun, second round, which is also the final round involved 5 sessions, with different focus. For some sessions, not be able to present my story completely, time was tight, and interviewers were rushing.
4 rounds:
ML breadth + Depth: Conceptual knowledge about ML and work experience discussion
Problem solving: Leetcode (Medium) + Basic ML concepts
Design + LP round: Use case discussion + Behavioural questions
Bar Raiser - LP round: Behavioural questions - Interviewer was non technical