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"The questions you are asked in an interview for a position as a scientist will depend greatly on field of science you intend to work in. Generally, interviewers will be interested in your formal education, field of study and specialization, work, internship, and research experience, scientific writing skills, and interest in the subject matter. Expect to be asked technical questions that pertain to the knowledge needed to perform the duties of the job. While there are some positions open to scientists who possess associates' or bachelors' degrees, most jobs will require you to have at least a masters' degree with the majority requiring you to have a doctorate."

54,376 scientist interview questions shared by candidates

- What do you know about us? - Phone screens: Going over resume, confirming interest/work - Case study - If you reach the final rounds, be prepared to word your answers according to the background of the person who is interviewing you (else you have just bored them and they completely missed the point)
avatar

Jr Data Scientist

Interviewed at Frontier

3.4★
May 12, 2021

- What do you know about us? - Phone screens: Going over resume, confirming interest/work - Case study - If you reach the final rounds, be prepared to word your answers according to the background of the person who is interviewing you (else you have just bored them and they completely missed the point)

A entrevista, para mim, não foi difícil, mas marquei como difícil, pois se você for mediano no tema, certamente não se sairá muito bem. Há perguntas para justificar uso de modelos, de abordagens, de técnicas de deep learning e Gen AI. Há, também, perguntas de como sua solução pode ser implementada em um cenário real (escala, custos financeiros, custos computacionais, manutenção...).
avatar

Senior Data Scientist

Interviewed at Blip

3.4★
Aug 1, 2024

A entrevista, para mim, não foi difícil, mas marquei como difícil, pois se você for mediano no tema, certamente não se sairá muito bem. Há perguntas para justificar uso de modelos, de abordagens, de técnicas de deep learning e Gen AI. Há, também, perguntas de como sua solução pode ser implementada em um cenário real (escala, custos financeiros, custos computacionais, manutenção...).

1. How do you use NN to reduce dimensionality? 2. Can you model time series as a linear regression model? 3. a) Can you use resampling methods like bagging to estimate the max of a population? b) Why is bagging a variance reduction scheme? 4. Why is the use of minibatch to minimize a function computationally more efficient than any other methods? 5.Gambler's ruin problem. 6. Assume that in a time series, some data are missing. How do you handle that? A. average out the existing values. Okay, so you want to average out the existing values, but how do you define the the new time series as a single function? A. Use characteristic or indicator function.
avatar

Data Scientist

Interviewed at Criteo

3.9★
Nov 22, 2018

1. How do you use NN to reduce dimensionality? 2. Can you model time series as a linear regression model? 3. a) Can you use resampling methods like bagging to estimate the max of a population? b) Why is bagging a variance reduction scheme? 4. Why is the use of minibatch to minimize a function computationally more efficient than any other methods? 5.Gambler's ruin problem. 6. Assume that in a time series, some data are missing. How do you handle that? A. average out the existing values. Okay, so you want to average out the existing values, but how do you define the the new time series as a single function? A. Use characteristic or indicator function.

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