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Data Scientist Interviews

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Data Scientist Interview Questions

In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.

Top Data Scientist Interview Questions & How to Answer

Question 1

Question #1: Which data modeling techniques do you prefer and why?

How to answer
How to answer: Turning data into understandable and actionable information is a critical part of the data scientist's job. This question allows employers to understand your data modeling skills and background. List and discuss your preferred data modeling techniques, including benefits such as ease of use, flexibility, etc.
Question 2

Question #2: How would you detect bogus Instagram accounts used for scamming consumers?

How to answer
How to answer: Questions like this one allow an employer to test your problem-solving skills. When answering open-ended questions such as these, feel free to ask clarifying questions and use whiteboards to demonstrate your coding and diagramming skills. Share your thought process as you work through the problem.
Question 3

Question #3: Describe circumstances that require a list, tuple, or set in Python.

How to answer
How to answer: Interviewers will use questions such as this one to test your Python programming skills. Review Python basics such as lists, tuples, and sets before your interview. You should be able to explain when and how each tool is used by data scientists.

54,373 data scientist interview questions shared by candidates

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.

Moderate level questions on the decisions to apply a particular ML algorithm on a dataset, (What they're looking for is a reasoning that factors in everything like inference time, results examinability(XAI or SHAP) and not just what algorithm will be the most accurate on a dataset. )
avatar

Data Scientist II

Interviewed at Fidelity Investments

4.1★
Mar 19, 2023

Moderate level questions on the decisions to apply a particular ML algorithm on a dataset, (What they're looking for is a reasoning that factors in everything like inference time, results examinability(XAI or SHAP) and not just what algorithm will be the most accurate on a dataset. )

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