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Data Scientist Interview Questions

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Data Scientist Interview Questions

In een sollicitatiegesprek voor de functie data scientist (M/V/X) kunt u verwachten dat de werkgever vragen stelt die uw vaardigheden voor gegevensmodellering, probleemoplossing en programmeren onderzoeken. Wees voorbereid op algemene vragen die uw kennis van statistiek en data science. Stel u ook in op open vragen die uw creativiteit, sociale vaardigheden en formele opleiding in gegevensmodellering en programmeren testen.

Meest gestelde sollicitatievragen voor een data scientist (M/V/X) en hoe te antwoorden

Question 1

Vraag 1: Welke gegevensmodelleertechnieken hebben uw voorkeur en waarom?

How to answer
Zo antwoordt u: Gegevens vertalen naar begrijpelijke en bruikbare informatie, is een essentieel onderdeel van de rol van een data scientist. Met deze vraag kunnen werkgevers uw gegevensmodelleringsvaardigheden en achtergrond doorgronden. Noem de voor u preferente gegevensmodelleringstechnieken en bespreek deze, bijvoorbeeld voordelen als gebruiksgemak, flexibiliteit, etc.
Question 2

Vraag 2: Hoe zou u nepaccounts op Instagram detecteren die gebruikt worden om consumenten op te lichten?

How to answer
Zo antwoordt u: Met zulke vragen kan een werkgever uw probleemoplossend vermogen testen. Bij het beantwoorden van open vragen als deze, is het prima om zelf naar verduidelijking te vragen en een whiteboard te gebruiken om te laten zien dat u kunt programmeren en dat u diagrammen kunt maken. Deel uw gedachtegang terwijl u de stappen van het probleem behandelt.
Question 3

Vraag 3: Beschrijf omstandigheden die een lijst, tupel of set in Python vereisen.

How to answer
Zo antwoordt u: Vraagstellers gebruiken dergelijke vragen om uw kennis van de programmeertaal Python te testen. Ga voor het sollicitatiegesprek de grondbeginselen van Python, zoals lijsten, tupels en sets. U zou moeten kunnen uitleggen wanneer en hoe elke tool door data scientists wordt gebruikt.

54,327 data scientist interview questions shared by candidates

How would you measure the health of Mentions, Facebook's app for celebrities? How can FB determine if it's worth it to keep using it? If a celebrity starts to use Mentions and begins interacting with their fans more, what part of the increase can be attributed to a celebrity using Mentions, and what part is just a celebrity wanting to get more involved in fan engagement?
avatar

Data Scientist

Interviewed at Meta

3.5★
Mar 29, 2017

How would you measure the health of Mentions, Facebook's app for celebrities? How can FB determine if it's worth it to keep using it? If a celebrity starts to use Mentions and begins interacting with their fans more, what part of the increase can be attributed to a celebrity using Mentions, and what part is just a celebrity wanting to get more involved in fan engagement?

Case Interview: the case is the car finance loan. - what are revenues and expenses - given a model that predicts when a customer is good (loan should be approved) or bad (loadn should be decline) find out: 1. the probability that the customer is good given the model predicts good 2. the probability that the customer is bad given the model is good 3. given a pentile graph of # of checked off loans / # of loans what is a better model than the current; what is the best model. Behavioral interview: - tell me about a time that you had to deal with changing objectives in your team/project - tell me about a time that you had to deal with unexpected problems in your project - tell me about a time that you had to persuase somebody Role interview: the case is a report on air company with low percentage of flight on time. Read the report an give an evaluation of it and some reccomendations to your boss. 15 minutes to read the report and remove anything unecessary or spot errors. 20 minutes to present it to your boss. 15 minutes to discuss afterwards from data scientist to data scientist.
avatar

Data Scientist Intern

Interviewed at Capital One

Oct 14, 2016

Case Interview: the case is the car finance loan. - what are revenues and expenses - given a model that predicts when a customer is good (loan should be approved) or bad (loadn should be decline) find out: 1. the probability that the customer is good given the model predicts good 2. the probability that the customer is bad given the model is good 3. given a pentile graph of # of checked off loans / # of loans what is a better model than the current; what is the best model. Behavioral interview: - tell me about a time that you had to deal with changing objectives in your team/project - tell me about a time that you had to deal with unexpected problems in your project - tell me about a time that you had to persuase somebody Role interview: the case is a report on air company with low percentage of flight on time. Read the report an give an evaluation of it and some reccomendations to your boss. 15 minutes to read the report and remove anything unecessary or spot errors. 20 minutes to present it to your boss. 15 minutes to discuss afterwards from data scientist to data scientist.

A set of values given: Assume table in SQL or list of dictionaries if using Python. Basically a row of data contained information: if it is post or it is a comment, row id and some other data. Find distribution of comments. #comments # posts 1 5000 2 6787 .. ..
avatar

Data Scientist

Interviewed at Meta

3.5★
Sep 27, 2017

A set of values given: Assume table in SQL or list of dictionaries if using Python. Basically a row of data contained information: if it is post or it is a comment, row id and some other data. Find distribution of comments. #comments # posts 1 5000 2 6787 .. ..

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